climeseries

Download, aggregate, process, and display monthly climatological data.

I don't care about the stupid package—where's the latest data?!

Okay! It's here. The "raw" data (as close as possible to the official source) is file climate-series_raw_yyyymmdd.csv; the data given as anomalies from a 1981–2010 baseline is file climate-series_yyyymmdd.csv. It's tabular data, arranged rows × columns for month/year × monthly series.

climeseries includes more than 1000 individual monthly climate time series; the full set of series names is listed below.

(If you're instead looking for the list of paleoclimate studies that use a variety of temperature proxies and methodologies in affirmation of Michael Mann's hockey stick result, you can find that here.)

Preliminaries

The climeseries R package is fairly easy to set up. In an R session:

install.packages("remotes") # If necessary.
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS = "true") # Probably unnecessary now. See:
## https://github.com/r-lib/remotes#environment-variables
remotes::install_github("priscian/climeseries")
library(climeseries)

## Once the package has been installed as described above, all you need to use it is:
library(climeseries)

Using climeseries

climeseries will by default store downloaded data sets in the current working directory (i.e. getwd()) of your R session, and will also load existing data sets from that directory. If you want to change climeseries's default directory, set the following option (with a directory of your choice) before you use climeseries:

options(climeseries_data_dir = "C:/common/data/climate/climeseries")

Now you're ready to go:

## Download a current climatological data set from the Internet.
inst <- get_climate_data(download = TRUE)
## N.B. I don't recommend the full download, which requires managing changing URLs &
##   download permissions; instead, use the latest data sets downloaded with the package,
##   or request an update from me. I'll make the download mechanism more flexible soon....

## Try loading this most recent data set from the default directory.
inst <- get_climate_data(download = FALSE, baseline = TRUE)

## Description of the data set returned by 'get_climate_data()'.
?get_climate_data

Note that get_climate_data() saves the current climatological data set, in the default directory, as two different file types: .RData and .csv; the .csv file is the most portable type and can be imported into other data-management software such as Microsoft Excel for plotting or further processing.

Making plots

climeseries has a pair of functions, plot_climate_data() and plot_models_and_climate_data(), to simplify plotting climate time series. Some examples follow.

########################################
## Plot several global instrumental temperature series.
########################################

airs_series <- "AIRS v7 Global"; baseline <- 1981:2010
new_airs <- interpolate_baseline(airs_series, baseline = baseline)
inst0 <- get_climate_data(download = FALSE, baseline = FALSE)
inst0 <- create_aggregate_variable(inst0, c("20th C. Reanalysis V3 Surface Air Global",
  "NCEP/DOE R2 Surface Air Global"),
  "20th C. Reanalysis V3–NCEP/DOE R2 Surface Air Global", type = "head")
inst0[[airs_series]] <- new_airs[[airs_series]]
series <- c("GISTEMP v4 Global", "NCEI Global", "HadCRUT5 Global",
  "BEST Global (Air Ice Temp.)", "JMA Global", "RSS TLT 4.0 -70.0/82.5",
  "UAH TLT 6.0 Global", "JRA-55 Surface Air Global", "ERA5 2m Global",
  "NCEP/NCAR R1 Surface Air Global", "20th C. Reanalysis V3–NCEP/DOE R2 Surface Air Global",
  "RATPAC-A Surface GLOBE", airs_series)
inst <- inst0 %>%
  dplyr::select(all_of(c(get_climate_series_names(inst0, invert = FALSE), series))) %>%
  recenter_anomalies(baseline = baseline, keep = series, skip = "AIRS v7 Global")
## N.B. Don't rebaseline here!
plot_climate_data(inst, series = series, 1880, yearly = TRUE, lwd = 2, ylim = c(-1.0, 1.0),
  save_png = FALSE)

########################################
## Plot global instrumental temperature series with 95% confidence intervals.
########################################

inst <- get_climate_data(download = FALSE, baseline = TRUE)
series <- c("BEST Global (Air Ice Temp.)", "HadCRUT5 Global")
plot_climate_data(inst, series = series, 1850, yearly = TRUE, lwd = 2, conf_int = TRUE,
  col = c("red", "blue"), alpha = 0.4, ci_alpha = 0.1, save_png = FALSE)

########################################
## Plot all CMIP5 scenario realizations, no instrumental temperature series.
########################################

inst <- get_climate_data(download = FALSE, baseline = TRUE)
cmip5 <- get_models_data(ensemble = "cmip5")
plot_models_and_climate_data(inst, cmip5, series = NULL, scenario = NULL, start = 1950,
  end = 2100.99, ma = 12, baseline = 1981:2010, center_fun = "mean", smooth_envelope = TRUE,
  col_m_mean = "red", ylim = c(-1, 5), save_png = FALSE)

########################################
## CMIP5 RCP 8.5 TAS + TOS scenario realizations compared to the primary land+SST series.
## Cf. Fig. 4(a) of Cowtan et al. 2015, dx.doi.org/10.1002/2015GL064888
########################################

inst <- get_climate_data(download = FALSE, baseline = TRUE)
cmip5 <- get_models_data(ensemble = "cmip5", subdir = "tas + tos")
series <- c("GISTEMP v4 Global", "NCEI Global", "HadCRUT5 Global",
  "BEST Global (Air Ice Temp.)", "JMA Global")
plot_models_and_climate_data(inst, cmip5, series = series, scenario = NULL, start = 1950,
  end = 2050.99, yearly = TRUE, ma = 12, baseline = 1986:2005, scenario_text =
  "Scenario TAS + TOS Realizations", center_fun = "mean", smooth_envelope = FALSE,
  envelope_type = "range", envelope_coverage = 0.90, envelope_text = "range",
  ylim = c(-0.75, 2.75), conf_int_i = FALSE, col_m_mean = grDevices::gray(0.8),
  alpha_envelope = 0.1, save_png = FALSE)

########################################
## Remove influence of exogenous factors characterizing ENSO, volcanic activity, and solar.
## Cf. Foster & Rahmstorf 2011, dx.doi.org/10.1088/1748-9326/6/4/044022
## Update 2024: https://tamino.wordpress.com/2024/02/16/adjusted-global-temperature-data/
########################################

inst <- get_climate_data(download = FALSE, baseline = FALSE)
inst <- create_aggregate_variable(inst, c("20th C. Reanalysis V3 Surface Air Global",
  "NCEP/DOE R2 Surface Air Global"),
  "20th C. Reanalysis V3–NCEP/DOE R2 Surface Air Global", type = "head")
series <- c("GISTEMP v4 Global", "NCEI Global", "HadCRUT5 Global",
  "BEST Global (Air Ice Temp.)", "JMA Global", "RSS TLT 4.0 -70.0/82.5",
  "UAH TLT 6.0 Global", "JRA-55 Surface Air Global", "ERA5 2m Global",
  "NCEP/NCAR R1 Surface Air Global", "20th C. Reanalysis V3–NCEP/DOE R2 Surface Air Global")
start <- 1950; end <- NULL
g <- remove_exogenous_influences(inst, series = series, start = start, end = end, max_lag = 12)
series_adj <- paste(series, "(adj.)")
main <- "Adjusted for ENSO, Volcanic, and Solar Influences"
plot_climate_data(g, series_adj, yearly = TRUE, main = main, type = "o", pch = 19, baseline = TRUE,
  save_png = FALSE)

## Plot several forcing variables
inst <- get_climate_data(download = FALSE, baseline = FALSE) %>%
  add_default_aggregate_variables()
forcings <- c("MEI Aggregate Global", "SAOD Aggregate Global", "TSI Aggregate Global",
  "CO2 Aggregate Global (interp.)")
forcings_scaled <- paste(forcings, "scaled")
forcings_start_year <- 1880
plyr::l_ply(forcings, function(a) { inst[[paste(a, "scaled")]] <<-
  inst[[a]] %>% `is.na<-`(inst$year < forcings_start_year) %>% `-`(min(., na.rm = TRUE)) %>%
    `/`(max(., na.rm = TRUE)) }) # Optimize white space
plot_climate_data(inst, series = forcings_scaled[1:4], start = forcings_start_year,
  ylab = "Normalized Ordinate", main = "Climate Forcings", ma = 12, yearly = FALSE, lwd = 2,
  col = c("darkblue", "darkgrey", "darkorange", "darkred"), save_png = FALSE)

########################################
## Estimate optimal no. & location of significant changepoints in piecewise regression of
##   climate series.
## Cf. Figure 1 of Cahill et al. 2015, dx.doi.org/10.1088/1748-9326/10/8/084002
########################################

inst <- get_climate_data(download = FALSE, baseline = TRUE)
series <- c("HadCRUT5 Global", "NCEI Global", "GISTEMP v4 Global", "JMA Global")
plot_climate_data(inst, series, yearly = TRUE, col = c("red", "purple", "blue", "green"), lwd = 1,
  segmented = TRUE, save_png = FALSE)

########################################
## Has sea-level rise accelerated?
## V. Church & White 2011, dx.doi.org/10.1007/s10712-011-9119-1.
## V. https://tamino.wordpress.com/2017/10/24/what-is-sea-level-up-to-lately
########################################

inst <- get_climate_data(download = FALSE, baseline = FALSE)
slr_series <- c("CSIRO Reconstructed Global Mean Sea Level", "AVISO Global Mean Sea Level")
slr <- purrr::reduce(
  list(
    inst,
    remove_periodic_cycle(inst, slr_series[1], center = FALSE, keep_series = FALSE,
      suffix = " (non-seasonal)"),
    remove_periodic_cycle(inst, slr_series[2], center = FALSE, keep_series = FALSE,
      suffix = " (non-seasonal)")
  ), dplyr::full_join) %>%
  dplyr::mutate(yr_part = year + (2 * month - 1)/24, .after = "month") %>%
  dplyr::arrange(year, month)
slr_baseline <- 1993:2013
slr <- create_aggregate_variable(slr, c("CSIRO Reconstructed Global Mean Sea Level (non-seasonal)",
  "AVISO Global Mean Sea Level (non-seasonal)"), "Global Mean Sea Level Aggregate",
  type = "head", baseline = slr_baseline)

sm <- fit_segmented_model(oss(slr, "Global Mean Sea Level Aggregate"),
  "Global Mean Sea Level Aggregate", yearly = TRUE, breakpoints... = list(h = 36, breaks = NULL))

slr_cols <- c("#1F78B4", "#33A02C")
slr_ylab <- sprintf("Global Mean Sea Level (mm) w.r.t %s–%s", min(slr_baseline), max(slr_baseline))
slr_main <- "Composite GMSL (Reconstruction + Satellite Altimetry)"
slr_end_callback <- expression({
  plot(sm$piecewise[["Global Mean Sea Level Aggregate"]]$sm, col = scales::alpha("red", 0.4),
    add = TRUE, rug = FALSE)
  psi <- sprintf(sm$piecewise[["Global Mean Sea Level Aggregate"]]$sm$psi[, 2], fmt = "%1.1f")
  vline(psi)
  ptbl <- segmented::slope(sm$piecewise[["Global Mean Sea Level Aggregate"]]$sm)$year %>%
    apply(2, sprintf, fmt = "%1.2f")
  colnames(ptbl)[1] <- "Rate (mm/y)"
  yr <- sm$piecewise[["Global Mean Sea Level Aggregate"]]$sm$model$year
  rownames(ptbl) <- c(min(yr), sort(rep(psi %>% as.numeric %>% round, 2)), max(yr)) %>%
    keystone::chunk(2) %>% sapply(paste, collapse = "–")
  ptbl %>% plotrix::addtable2plot(x = 1940, y = -200, table = ., cex = 0.8, bg = "lightgray",
    display.rownames = TRUE)
})
plot_climate_data(slr, series = paste(slr_series, "(non-seasonal)"), yearly = TRUE,
  baseline = slr_baseline, conf_int = TRUE, col = slr_cols, lwd = 2, main = slr_main,
  ylab = slr_ylab, ylim = NULL, end_callback = slr_end_callback, save_png = FALSE)

########################################
## Calculate global average land temperature anomalies from GHCN-m station data, w/ 95% CI.
## SOP:
## • For each station/month, calc 1951–1980 baseline
## • Compute temp anomalies by subtracting station/month baseline from absolute temps
## • Split region into lat-long grid
## • For each grid cell, get monthly avg of all anomalies
## • Lat-weight each cell & calc monthly region avg
########################################

## If 'download' = TRUE, import & prep data from https://www.ncei.noaa.gov/pub/data/ghcn/v4/
## (It will take a while, so be patient; later, you can use 'download = FALSE'.)
download <- TRUE
ghcn_v4_avg_f <- new.env()
get_series_from_ghcn_gridded(ver = 4, temp = "avg", quality = "f", load_env = ghcn_v4_avg_f,
  download = download)

ghcn_v4_avg_u <- new.env()
get_series_from_ghcn_gridded(ver = 4, temp = "avg", quality = "u", load_env = ghcn_v4_avg_u,
  download = download)

## Select only stations (e.g.) which are longer-term over the coverage period
coverage_years <- NULL # Default: keep all stations
meets_filter_criteria_u <- make_coverage_filter(ghcn_v4_avg_u$ghcn, coverage_years,
  min_nonmissing_months = 12, min_nonmissing_years_prop = 0.9)
meets_filter_criteria_u %>% table
meets_filter_criteria_f <- make_coverage_filter(ghcn_v4_avg_f$ghcn, coverage_years,
  min_nonmissing_months = 12, min_nonmissing_years_prop = 0.9)
meets_filter_criteria_f %>% table

## In the case of adj. v unadj., however, the temporal coverage is usu. different;
##   so for comparison, let's use the unadj. stations for both series.
gf <-
  ghcn_v4_avg_f$ghcn %>% dplyr::select(any_of(c(get_climate_series_names(., invert = FALSE),
  meets_filter_criteria_u[meets_filter_criteria_u] %>% names)))

lat_range <- c(90, -90); long_range <- c(-180, 180) # Default global coverage
#lat_range <- c(0, -90); long_range <- c(-180, 180) # Southern hemisphere
#lat_range <- c(37, 71); long_range <- c(-22, 45) # Europe
#lat_range <- c(90, 60); long_range <- c(-180, 180) # Arctic
#lat_range <- c(50, 25); long_range <- c(-125, -70) # USA
round_to_nearest <- NULL #1.0

## Supply paths to XLSX files to store portable versions of data & results:
spreadsheet_path_f <- spreadsheet_path_u <- NULL

grid_size <- c(5, 5)
use_lat_zonal_weights <- TRUE

## Raw data
uadj_ghcn_v4_avg_u <-
  make_ghcn_temperature_series(ghcn_v4_avg_u$ghcn, ghcn_v4_avg_u$station_metadata,
    other_filters = meets_filter_criteria_u, grid_size = grid_size, lat_range = lat_range,
    long_range = long_range, make_planetary_grid... = list(use_lat_weights = TRUE),
    use_lat_zonal_weights = use_lat_zonal_weights, uncertainty = TRUE, boot_seed = NULL,
    round_to_nearest = round_to_nearest, spreadsheet_path = spreadsheet_path_u)

## Homogenized/adjusted data
adj_ghcn_v4_avg_f <-
  make_ghcn_temperature_series(gf, ghcn_v4_avg_f$station_metadata, grid_size = grid_size,
    lat_range = lat_range, long_range = long_range,
    make_planetary_grid... = list(use_lat_weights = TRUE),
    use_lat_zonal_weights = use_lat_zonal_weights, uncertainty = TRUE, boot_seed = NULL,
    round_to_nearest = round_to_nearest, spreadsheet_path = spreadsheet_path_f)

## Collect both data sets & equivalent official series for comparison
inst <- get_climate_data(download = FALSE, baseline = FALSE) %>%
  { purrr::reduce(list(., adj_ghcn_v4_avg_f, uadj_ghcn_v4_avg_u), dplyr::full_join,
    by = c("month", "year")) } %>%
  dplyr::mutate(yr_part = year + (month - 0.5)/12, met_year = NA)

series <- c(names(adj_ghcn_v4_avg_f)[3], names(uadj_ghcn_v4_avg_u)[3],
  "GISTEMP v4 Global Land")

extra_trends <- sapply(series[1:2],
  function(a) { list(range = c(1970, current_year - 0.01), lwd = 2) }, simplify = FALSE)
## Plot both data sets & equivalent official series for comparison
r <-
  plot_climate_data(inst, series = series[1:3], 1850, yearly = TRUE, baseline = 1981:2010,
    conf_int = TRUE, conf_int_series = NULL, ci_alpha = c(0.2, 0.2, 0.2), lwd = c(2, 2),
    ylim = NULL, alpha = c(1, 1, 1), trend = TRUE, trend... = list(keep_default_trends = FALSE,
    rate_expression =
      sprintf("expression(Delta ~ \"= %%+1.2f ± %%1.2f %s/dec. %s\")", "°C", "1970–" %_%
        (current_year - 1))),
    extra_trends = extra_trends, trend_legend_inset = c(0.2, 0.01),
    make_standardized_plot_filename... = list(suffix = "1970_adj-v-unadj-all"),
    save_png = FALSE)

## Station counts
station_counts <- get_station_counts(x = uadj_ghcn_v4_avg_u, env = ghcn_v4_avg_u,
  region_name = "v4 Global Complete", start_year = 1850, end_year = current_year - 0.01,
  save_png = FALSE)
## Max station count for a single month:
station_counts$station_counts_series$`station count` %>% max(na.rm = TRUE) %>% print
## Total no. of stations used in average temp series:
station_counts$station_series %>% get_climate_series_names %>% length %>% print

## Station distribution
plot_stations_map(attr(uadj_ghcn_v4_avg_u, "filtered_metadata"), region_name = "global",
  title_text = sprintf("GHCN-m v4 global station distribution"), save_png = FALSE)

More information

climeseries is presented here as a working beta. For more information on what the package offers, check out

library(help = climeseries)

from the R command line.

Data sets

The latest data sets downloaded by me (where "latest" means whenever I've gotten around to updating them) can be found here: Current "climeseries" data. Older data sets are listed here, too.

Latest column names

The current column names—the names of the monthly climatological data sets—are given below. You will eventually find more information on each data set from the R command line via:

?get_climate_data

1. year
2. met_year
3. yr_part
4. month
5. 20th C. Reanalysis V3 Sea Surface Global
6. 20th C. Reanalysis V3 Sea Surface NH
7. 20th C. Reanalysis V3 Sea Surface SH
8. 20th C. Reanalysis V3 Surface Air Global
9. 20th C. Reanalysis V3 Surface Air Global Land
10. 20th C. Reanalysis V3 Surface Air Global Ocean
11. 20th C. Reanalysis V3 Surface Air NH
12. 20th C. Reanalysis V3 Surface Air NH Land
13. 20th C. Reanalysis V3 Surface Air NH Ocean
14. 20th C. Reanalysis V3 Surface Air NH Polar
15. 20th C. Reanalysis V3 Surface Air NH Polar Land
16. 20th C. Reanalysis V3 Surface Air NH Polar Ocean
17. 20th C. Reanalysis V3 Surface Air SH
18. 20th C. Reanalysis V3 Surface Air SH Land
19. 20th C. Reanalysis V3 Surface Air SH Ocean
20. 20th C. Reanalysis V3 Surface Air SH Polar
21. 20th C. Reanalysis V3 Surface Air SH Polar Land
22. 20th C. Reanalysis V3 Surface Air SH Polar Ocean
23. 20th C. Reanalysis V3 Surface Air Tropics
24. 20th C. Reanalysis V3 Surface Air Tropics Land
25. 20th C. Reanalysis V3 Surface Air Tropics Ocean
26. 20th C. Reanalysis V3 Surface Air USA 48
27. 20th C. Reanalysis V3 Surface Air USA 48 Land
28. 20th C. Reanalysis V3 Surface Air USA 48 Ocean
29. AIRS v6 Global
30. AIRS v7 Global
31. AIRS v6 N.Hemi
32. AIRS v7 N.Hemi
33. AIRS v6 S.Hemi
34. AIRS v7 S.Hemi
35. AIRS v6 Zonal Glob
36. AIRS v6 Zonal N.Hemi
37. AIRS v6 Zonal S.Hemi
38. AIRS v6 Zonal 24N-90N
39. AIRS v6 Zonal 24S-24N
40. AIRS v6 Zonal 90S-24S
41. AIRS v6 Zonal 64N-90N
42. AIRS v6 Zonal 44N-64N
43. AIRS v6 Zonal 24N-44N
44. AIRS v6 Zonal EQU-24N
45. AIRS v6 Zonal 24S-EQU
46. AIRS v6 Zonal 44S-24S
47. AIRS v6 Zonal 64S-44S
48. AIRS v6 Zonal 90S-64S
49. AIRS v7 Zonal Glob
50. AIRS v7 Zonal N.Hemi
51. AIRS v7 Zonal S.Hemi
52. AIRS v7 Zonal 24N-90N
53. AIRS v7 Zonal 24S-24N
54. AIRS v7 Zonal 90S-24S
55. AIRS v7 Zonal 64N-90N
56. AIRS v7 Zonal 44N-64N
57. AIRS v7 Zonal 24N-44N
58. AIRS v7 Zonal EQU-24N
59. AIRS v7 Zonal 24S-EQU
60. AIRS v7 Zonal 44S-24S
61. AIRS v7 Zonal 64S-44S
62. AIRS v7 Zonal 90S-64S
63. Antarctica Land Ice Mass Variation
64. Antarctica Land Ice Mass Variation_uncertainty
65. AVISO Global Mean Sea Level
66. AVISO Global Mean Sea Level (nonseasonal)
67. BEST Antarctica
68. BEST Antarctica_uncertainty
69. BEST Global (Air Ice Temp.)
70. BEST Global (Water Ice Temp.)
71. BEST Global (Air Ice Temp.)_uncertainty
72. BEST Global (Water Ice Temp.)_uncertainty
73. BEST Global Land
74. BEST Global Land_uncertainty
75. BEST Greenland
76. BEST Greenland_uncertainty
77. BE Land+SST (Air Ice Temp.) (0N-90N, 180W-180E)
78. BE Land+SST (Air Ice Temp.) (90S-0N, 180W-180E)
79. BEST NH Land
80. BEST NH Land_uncertainty
81. BEST SH Land
82. BEST SH Land_uncertainty
83. BEST US
84. BEST US_uncertainty
85. CO2 Cape Grim
86. CO2 Cape Grim_uncertainty
87. CO2 Mauna Loa
88. CO2 NOAA ESRL
89. Cowtan & Way Krig. Global
90. Cowtan & Way Krig. Global_uncertainty
91. Cowtan & Way Krig. Global Land
92. CRUTEM4 Global
93. CRUTEM4 NH
94. CRUTEM4 SH
95. CRUTEM4v Global
96. CRUTEM4v NH
97. CRUTEM4v SH
98. CRUTEM5 Global
99. CRUTEM5 Global_uncertainty
100. CRUTEM5 NH
101. CRUTEM5 NH_uncertainty
102. CRUTEM5 SH
103. CRUTEM5 SH_uncertainty
104. CSIRO Global Mean Sea Level
105. CSIRO Reconstructed Global Mean Sea Level
106. CSIRO Reconstructed Global Mean Sea Level_uncertainty
107. ERA5 2m Global
108. ERA5 2m European
109. ERA5 Surface Air Global
110. ERA5 Surface Air Global Land
111. ERA5 Surface Air Global Ocean
112. ERA5 Surface Air NH
113. ERA5 Surface Air NH Land
114. ERA5 Surface Air NH Ocean
115. ERA5 Surface Air NH Polar
116. ERA5 Surface Air NH Polar Land
117. ERA5 Surface Air NH Polar Ocean
118. ERA5 Surface Air SH
119. ERA5 Surface Air SH Land
120. ERA5 Surface Air SH Ocean
121. ERA5 Surface Air SH Polar
122. ERA5 Surface Air SH Polar Land
123. ERA5 Surface Air SH Polar Ocean
124. ERA5 Surface Air Tropics
125. ERA5 Surface Air Tropics Land
126. ERA5 Surface Air Tropics Ocean
127. ERA5 Surface Air USA 48
128. ERA5 Surface Air USA 48 Land
129. ERA5 Surface Air USA 48 Ocean
130. ESRL AMO
131. Extended Multivariate ENSO Index
132. GISS Stratospheric Aerosol Optical Depth (550 nm) Global
133. GISS Stratospheric Aerosol Optical Depth (550 nm) NH
134. GISS Stratospheric Aerosol Optical Depth (550 nm) SH
135. GISTEMP v3 Global
136. GISTEMP v3 Global Land
137. GISTEMP v3 NH
138. GISTEMP v3 NH Land
139. GISTEMP v3 SH
140. GISTEMP v3 SH Land
141. GISTEMP v3 Zonal Glob
142. GISTEMP v3 Zonal NHem
143. GISTEMP v3 Zonal SHem
144. GISTEMP v3 Zonal 24N-90N
145. GISTEMP v3 Zonal 24S-24N
146. GISTEMP v3 Zonal 90S-24S
147. GISTEMP v3 Zonal 64N-90N
148. GISTEMP v3 Zonal 44N-64N
149. GISTEMP v3 Zonal 24N-44N
150. GISTEMP v3 Zonal EQU-24N
151. GISTEMP v3 Zonal 24S-EQU
152. GISTEMP v3 Zonal 44S-24S
153. GISTEMP v3 Zonal 64S-44S
154. GISTEMP v3 Zonal 90S-64S
155. GISTEMP v3 Zonal Land Glob
156. GISTEMP v3 Zonal Land NHem
157. GISTEMP v3 Zonal Land SHem
158. GISTEMP v3 Zonal Land 24N-90N
159. GISTEMP v3 Zonal Land 24S-24N
160. GISTEMP v3 Zonal Land 90S-24S
161. GISTEMP v3 Zonal Land 64N-90N
162. GISTEMP v3 Zonal Land 44N-64N
163. GISTEMP v3 Zonal Land 24N-44N
164. GISTEMP v3 Zonal Land EQU-24N
165. GISTEMP v3 Zonal Land 24S-EQU
166. GISTEMP v3 Zonal Land 44S-24S
167. GISTEMP v3 Zonal Land 64S-44S
168. GISTEMP v3 Zonal Land 90S-64S
169. GISTEMP v4 Global
170. GISTEMP v4 Global Land
171. GISTEMP v4 NH
172. GISTEMP v4 NH Land
173. GISTEMP v4 SH
174. GISTEMP v4 SH Land
175. GISTEMP v4 Zonal Glob
176. GISTEMP v4 Zonal NHem
177. GISTEMP v4 Zonal SHem
178. GISTEMP v4 Zonal 24N-90N
179. GISTEMP v4 Zonal 24S-24N
180. GISTEMP v4 Zonal 90S-24S
181. GISTEMP v4 Zonal 64N-90N
182. GISTEMP v4 Zonal 44N-64N
183. GISTEMP v4 Zonal 24N-44N
184. GISTEMP v4 Zonal EQU-24N
185. GISTEMP v4 Zonal 24S-EQU
186. GISTEMP v4 Zonal 44S-24S
187. GISTEMP v4 Zonal 64S-44S
188. GISTEMP v4 Zonal 90S-64S
189. GISTEMP v4 Zonal Land Glob
190. GISTEMP v4 Zonal Land NHem
191. GISTEMP v4 Zonal Land SHem
192. GISTEMP v4 Zonal Land 24N-90N
193. GISTEMP v4 Zonal Land 24S-24N
194. GISTEMP v4 Zonal Land 90S-24S
195. GISTEMP v4 Zonal Land 64N-90N
196. GISTEMP v4 Zonal Land 44N-64N
197. GISTEMP v4 Zonal Land 24N-44N
198. GISTEMP v4 Zonal Land EQU-24N
199. GISTEMP v4 Zonal Land 24S-EQU
200. GISTEMP v4 Zonal Land 44S-24S
201. GISTEMP v4 Zonal Land 64S-44S
202. GISTEMP v4 Zonal Land 90S-64S
203. GRACE-FO Antarctic Ice Mass global [Gt]
204. GRACE-FO Antarctic Ice Mass uncertainty_global [Gt]
205. GRACE-FO Antarctic Ice Mass 301 [Gt]
206. GRACE-FO Antarctic Ice Mass uncertainty_301 [Gt]
207. GRACE-FO Antarctic Ice Mass 302 [Gt]
208. GRACE-FO Antarctic Ice Mass uncertainty_302 [Gt]
209. GRACE-FO Antarctic Ice Mass 303 [Gt]
210. GRACE-FO Antarctic Ice Mass uncertainty_303 [Gt]
211. GRACE-FO Antarctic Ice Mass 304 [Gt]
212. GRACE-FO Antarctic Ice Mass uncertainty_304 [Gt]
213. GRACE-FO Antarctic Ice Mass 305 [Gt]
214. GRACE-FO Antarctic Ice Mass uncertainty_305 [Gt]
215. GRACE-FO Antarctic Ice Mass 306 [Gt]
216. GRACE-FO Antarctic Ice Mass uncertainty_306 [Gt]
217. GRACE-FO Antarctic Ice Mass 307 [Gt]
218. GRACE-FO Antarctic Ice Mass uncertainty_307 [Gt]
219. GRACE-FO Antarctic Ice Mass 308 [Gt]
220. GRACE-FO Antarctic Ice Mass uncertainty_308 [Gt]
221. GRACE-FO Antarctic Ice Mass 309 [Gt]
222. GRACE-FO Antarctic Ice Mass uncertainty_309 [Gt]
223. GRACE-FO Antarctic Ice Mass 310 [Gt]
224. GRACE-FO Antarctic Ice Mass uncertainty_310 [Gt]
225. GRACE-FO Antarctic Ice Mass 311 [Gt]
226. GRACE-FO Antarctic Ice Mass uncertainty_311 [Gt]
227. GRACE-FO Antarctic Ice Mass 312 [Gt]
228. GRACE-FO Antarctic Ice Mass uncertainty_312 [Gt]
229. GRACE-FO Antarctic Ice Mass 313 [Gt]
230. GRACE-FO Antarctic Ice Mass uncertainty_313 [Gt]
231. GRACE-FO Antarctic Ice Mass 314 [Gt]
232. GRACE-FO Antarctic Ice Mass uncertainty_314 [Gt]
233. GRACE-FO Antarctic Ice Mass 315 [Gt]
234. GRACE-FO Antarctic Ice Mass uncertainty_315 [Gt]
235. GRACE-FO Antarctic Ice Mass 316 [Gt]
236. GRACE-FO Antarctic Ice Mass uncertainty_316 [Gt]
237. GRACE-FO Antarctic Ice Mass 317 [Gt]
238. GRACE-FO Antarctic Ice Mass uncertainty_317 [Gt]
239. GRACE-FO Antarctic Ice Mass 318 [Gt]
240. GRACE-FO Antarctic Ice Mass uncertainty_318 [Gt]
241. GRACE-FO Antarctic Ice Mass 319 [Gt]
242. GRACE-FO Antarctic Ice Mass uncertainty_319 [Gt]
243. GRACE-FO Antarctic Ice Mass 320 [Gt]
244. GRACE-FO Antarctic Ice Mass uncertainty_320 [Gt]
245. GRACE-FO Antarctic Ice Mass 321 [Gt]
246. GRACE-FO Antarctic Ice Mass uncertainty_321 [Gt]
247. GRACE-FO Antarctic Ice Mass 322 [Gt]
248. GRACE-FO Antarctic Ice Mass uncertainty_322 [Gt]
249. GRACE-FO Antarctic Ice Mass 323 [Gt]
250. GRACE-FO Antarctic Ice Mass uncertainty_323 [Gt]
251. GRACE-FO Antarctic Ice Mass 324 [Gt]
252. GRACE-FO Antarctic Ice Mass uncertainty_324 [Gt]
253. GRACE-FO Antarctic Ice Mass 325 [Gt]
254. GRACE-FO Antarctic Ice Mass uncertainty_325 [Gt]
255. GRACE-FO Greenland Ice Mass global [Gt]
256. GRACE-FO Greenland Ice Mass uncertainty_global [Gt]
257. GRACE-FO Greenland Ice Mass 301 [Gt]
258. GRACE-FO Greenland Ice Mass uncertainty_301 [Gt]
259. GRACE-FO Greenland Ice Mass 302 [Gt]
260. GRACE-FO Greenland Ice Mass uncertainty_302 [Gt]
261. GRACE-FO Greenland Ice Mass 303 [Gt]
262. GRACE-FO Greenland Ice Mass uncertainty_303 [Gt]
263. GRACE-FO Greenland Ice Mass 304 [Gt]
264. GRACE-FO Greenland Ice Mass uncertainty_304 [Gt]
265. GRACE-FO Greenland Ice Mass 305 [Gt]
266. GRACE-FO Greenland Ice Mass uncertainty_305 [Gt]
267. GRACE-FO Greenland Ice Mass 306 [Gt]
268. GRACE-FO Greenland Ice Mass uncertainty_306 [Gt]
269. GRACE-FO Greenland Ice Mass 307 [Gt]
270. GRACE-FO Greenland Ice Mass uncertainty_307 [Gt]
271. Greenland Land Ice Mass Variation
272. Greenland Land Ice Mass Variation_uncertainty
273. HadCET
274. HadCRUT4 Global
275. HadCRUT4 Global_uncertainty
276. HadCRUT4 NH
277. HadCRUT4 NH_uncertainty
278. HadCRUT4 SH
279. HadCRUT4 SH_uncertainty
280. HadCRUT4 Tropics
281. HadCRUT4 Tropics_uncertainty
282. HadCRUT5 Global
283. HadCRUT5 Global_uncertainty
284. HadCRUT5 Global (not infilled)
285. HadCRUT5 Global (not infilled)_uncertainty
286. HadCRUT5 NH
287. HadCRUT5 NH_uncertainty
288. HadCRUT5 NH (not infilled)
289. HadCRUT5 NH (not infilled)_uncertainty
290. HadCRUT5 SH
291. HadCRUT5 SH_uncertainty
292. HadCRUT5 SH (not infilled)
293. HadCRUT5 SH (not infilled)_uncertainty
294. HadSST3 Global
295. HadSST3 Global_uncertainty
296. HadSST3 NH
297. HadSST3 NH_uncertainty
298. HadSST3 SH
299. HadSST3 SH_uncertainty
300. HadSST3 Tropics
301. HadSST3 Tropics_uncertainty
302. HadSST4 Global
303. HadSST4 Global_uncertainty
304. HadSST4 NH
305. HadSST4 NH_uncertainty
306. HadSST4 SH
307. HadSST4 SH_uncertainty
308. HadSST4 Tropics
309. HadSST4 Tropics_uncertainty
310. JMA Global
311. JRA-55 Surface Air Global
312. JRA-55 Surface Air Global Land
313. JRA-55 Surface Air Global Ocean
314. JRA-55 Surface Air NH
315. JRA-55 Surface Air NH Land
316. JRA-55 Surface Air NH Ocean
317. JRA-55 Surface Air NH Polar
318. JRA-55 Surface Air NH Polar Land
319. JRA-55 Surface Air NH Polar Ocean
320. JRA-55 Surface Air SH
321. JRA-55 Surface Air SH Land
322. JRA-55 Surface Air SH Ocean
323. JRA-55 Surface Air SH Polar
324. JRA-55 Surface Air SH Polar Land
325. JRA-55 Surface Air SH Polar Ocean
326. JRA-55 Surface Air Tropics
327. JRA-55 Surface Air Tropics Land
328. JRA-55 Surface Air Tropics Ocean
329. JRA-55 Surface Air USA 48
330. JRA-55 Surface Air USA 48 Land
331. JRA-55 Surface Air USA 48 Ocean
332. MERRA-2 Surface Air Global
333. MERRA-2 Surface Air Global Land
334. MERRA-2 Surface Air Global Ocean
335. MERRA-2 Surface Air NH
336. MERRA-2 Surface Air NH Land
337. MERRA-2 Surface Air NH Ocean
338. MERRA-2 Surface Air NH Polar
339. MERRA-2 Surface Air NH Polar Land
340. MERRA-2 Surface Air NH Polar Ocean
341. MERRA-2 Surface Air SH
342. MERRA-2 Surface Air SH Land
343. MERRA-2 Surface Air SH Ocean
344. MERRA-2 Surface Air SH Polar
345. MERRA-2 Surface Air SH Polar Land
346. MERRA-2 Surface Air SH Polar Ocean
347. MERRA-2 Surface Air Tropics
348. MERRA-2 Surface Air Tropics Land
349. MERRA-2 Surface Air Tropics Ocean
350. MERRA-2 Surface Air USA 48
351. MERRA-2 Surface Air USA 48 Land
352. MERRA-2 Surface Air USA 48 Ocean
353. Multivariate ENSO Index
354. NCEI Global
355. NCEI Global Land
356. NCEI Global Ocean
357. NCEI NH
358. NCEI NH Land
359. NCEI NH Ocean
360. NCEI Atlantic Ocean Heat Content 0-2000m
361. NCEI Atlantic Ocean Heat Content 0-2000m NH
362. NCEI Atlantic Ocean Heat Content 0-2000m SH
363. NCEI Atlantic Ocean Heat Content 0-700m
364. NCEI Atlantic Ocean Heat Content 0-700m NH
365. NCEI Atlantic Ocean Heat Content 0-700m SH
366. NCEI Global Ocean Heat Content 0-2000m
367. NCEI Global Ocean Heat Content 0-2000m NH
368. NCEI Global Ocean Heat Content 0-2000m SH
369. NCEI Global Ocean Heat Content 0-700m
370. NCEI Global Ocean Heat Content 0-700m NH
371. NCEI Global Ocean Heat Content 0-700m SH
372. NCEI Indian Ocean Heat Content 0-2000m
373. NCEI Indian Ocean Heat Content 0-2000m NH
374. NCEI Indian Ocean Heat Content 0-2000m SH
375. NCEI Indian Ocean Heat Content 0-700m
376. NCEI Indian Ocean Heat Content 0-700m NH
377. NCEI Indian Ocean Heat Content 0-700m SH
378. NCEI Pacific Ocean Heat Content 0-2000m
379. NCEI Pacific Ocean Heat Content 0-2000m NH
380. NCEI Pacific Ocean Heat Content 0-2000m SH
381. NCEI Pacific Ocean Heat Content 0-700m
382. NCEI Pacific Ocean Heat Content 0-700m NH
383. NCEI Pacific Ocean Heat Content 0-700m SH
384. NCEI Atlantic Ocean Heat Content 0-2000m (Pentadal)
385. NCEI Atlantic Ocean Heat Content 0-2000m NH (Pentadal)
386. NCEI Atlantic Ocean Heat Content 0-2000m SH (Pentadal)
387. NCEI Atlantic Ocean Heat Content 0-700m (Pentadal)
388. NCEI Atlantic Ocean Heat Content 0-700m NH (Pentadal)
389. NCEI Atlantic Ocean Heat Content 0-700m SH (Pentadal)
390. NCEI Global Ocean Heat Content 0-2000m (Pentadal)
391. NCEI Global Ocean Heat Content 0-2000m NH (Pentadal)
392. NCEI Global Ocean Heat Content 0-2000m SH (Pentadal)
393. NCEI Global Ocean Heat Content 0-700m (Pentadal)
394. NCEI Global Ocean Heat Content 0-700m NH (Pentadal)
395. NCEI Global Ocean Heat Content 0-700m SH (Pentadal)
396. NCEI Indian Ocean Heat Content 0-2000m (Pentadal)
397. NCEI Indian Ocean Heat Content 0-2000m NH (Pentadal)
398. NCEI Indian Ocean Heat Content 0-2000m SH (Pentadal)
399. NCEI Indian Ocean Heat Content 0-700m (Pentadal)
400. NCEI Indian Ocean Heat Content 0-700m NH (Pentadal)
401. NCEI Indian Ocean Heat Content 0-700m SH (Pentadal)
402. NCEI Pacific Ocean Heat Content 0-2000m (Pentadal)
403. NCEI Pacific Ocean Heat Content 0-2000m NH (Pentadal)
404. NCEI Pacific Ocean Heat Content 0-2000m SH (Pentadal)
405. NCEI Pacific Ocean Heat Content 0-700m (Pentadal)
406. NCEI Pacific Ocean Heat Content 0-700m NH (Pentadal)
407. NCEI Pacific Ocean Heat Content 0-700m SH (Pentadal)
408. NCEI Atlantic Ocean Heat Content 0-2000m_uncertainty
409. NCEI Atlantic Ocean Heat Content 0-2000m NH_uncertainty
410. NCEI Atlantic Ocean Heat Content 0-2000m SH_uncertainty
411. NCEI Atlantic Ocean Heat Content 0-700m_uncertainty
412. NCEI Atlantic Ocean Heat Content 0-700m NH_uncertainty
413. NCEI Atlantic Ocean Heat Content 0-700m SH_uncertainty
414. NCEI Global Ocean Heat Content 0-2000m_uncertainty
415. NCEI Global Ocean Heat Content 0-2000m NH_uncertainty
416. NCEI Global Ocean Heat Content 0-2000m SH_uncertainty
417. NCEI Global Ocean Heat Content 0-700m_uncertainty
418. NCEI Global Ocean Heat Content 0-700m NH_uncertainty
419. NCEI Global Ocean Heat Content 0-700m SH_uncertainty
420. NCEI Indian Ocean Heat Content 0-2000m_uncertainty
421. NCEI Indian Ocean Heat Content 0-2000m NH_uncertainty
422. NCEI Indian Ocean Heat Content 0-2000m SH_uncertainty
423. NCEI Indian Ocean Heat Content 0-700m_uncertainty
424. NCEI Indian Ocean Heat Content 0-700m NH_uncertainty
425. NCEI Indian Ocean Heat Content 0-700m SH_uncertainty
426. NCEI Pacific Ocean Heat Content 0-2000m_uncertainty
427. NCEI Pacific Ocean Heat Content 0-2000m NH_uncertainty
428. NCEI Pacific Ocean Heat Content 0-2000m SH_uncertainty
429. NCEI Pacific Ocean Heat Content 0-700m_uncertainty
430. NCEI Pacific Ocean Heat Content 0-700m NH_uncertainty
431. NCEI Pacific Ocean Heat Content 0-700m SH_uncertainty
432. NCEI Atlantic Ocean Heat Content 0-2000m (Pentadal)_uncertainty
433. NCEI Atlantic Ocean Heat Content 0-2000m NH (Pentadal)_uncertainty
434. NCEI Atlantic Ocean Heat Content 0-2000m SH (Pentadal)_uncertainty
435. NCEI Atlantic Ocean Heat Content 0-700m (Pentadal)_uncertainty
436. NCEI Atlantic Ocean Heat Content 0-700m NH (Pentadal)_uncertainty
437. NCEI Atlantic Ocean Heat Content 0-700m SH (Pentadal)_uncertainty
438. NCEI Global Ocean Heat Content 0-2000m (Pentadal)_uncertainty
439. NCEI Global Ocean Heat Content 0-2000m NH (Pentadal)_uncertainty
440. NCEI Global Ocean Heat Content 0-2000m SH (Pentadal)_uncertainty
441. NCEI Global Ocean Heat Content 0-700m (Pentadal)_uncertainty
442. NCEI Global Ocean Heat Content 0-700m NH (Pentadal)_uncertainty
443. NCEI Global Ocean Heat Content 0-700m SH (Pentadal)_uncertainty
444. NCEI Indian Ocean Heat Content 0-2000m (Pentadal)_uncertainty
445. NCEI Indian Ocean Heat Content 0-2000m NH (Pentadal)_uncertainty
446. NCEI Indian Ocean Heat Content 0-2000m SH (Pentadal)_uncertainty
447. NCEI Indian Ocean Heat Content 0-700m (Pentadal)_uncertainty
448. NCEI Indian Ocean Heat Content 0-700m NH (Pentadal)_uncertainty
449. NCEI Indian Ocean Heat Content 0-700m SH (Pentadal)_uncertainty
450. NCEI Pacific Ocean Heat Content 0-2000m (Pentadal)_uncertainty
451. NCEI Pacific Ocean Heat Content 0-2000m NH (Pentadal)_uncertainty
452. NCEI Pacific Ocean Heat Content 0-2000m SH (Pentadal)_uncertainty
453. NCEI Pacific Ocean Heat Content 0-700m (Pentadal)_uncertainty
454. NCEI Pacific Ocean Heat Content 0-700m NH (Pentadal)_uncertainty
455. NCEI Pacific Ocean Heat Content 0-700m SH (Pentadal)_uncertainty
456. NCEI SH
457. NCEI SH Land
458. NCEI SH Ocean
459. NCEI US Avg. Temp.
460. NCEI US Max. Temp.
461. NCEI US Min. Temp.
462. NCEI US Palmer Z-Index
463. NCEI US PDSI
464. NCEI US PHDI
465. NCEI US PMDI
466. NCEI US Precip.
467. NCEI v4 Land 60S-30S
468. NCEI v4 Land 60N-90N
469. NCEI v4 Land 60S-60N
470. NCEI v4 Land 90S-00N
471. NCEI v4 Land 90S-20S
472. NCEI v4 Land 90S-60S
473. NCEI v4 Land 90S-90N
474. NCEI v4 Land + Ocean 00N-90N
475. NCEI v4 Land + Ocean 00N-30N
476. NCEI v4 Land + Ocean 20N-90N
477. NCEI v4 Land + Ocean 20S-20N
478. NCEI v4 Land + Ocean 30N-60N
479. NCEI v4 Land + Ocean 30S-00N
480. NCEI v4 Land 00N-30N
481. NCEI v4 Land 00N-90N
482. NCEI v4 Land 20N-90N
483. NCEI v4 Land 20S-20N
484. NCEI v4 Land 30N-60N
485. NCEI v4 Land + Ocean 60N-90N
486. NCEI v4 Land + Ocean 60S-30S
487. NCEI v4 Land + Ocean 60S-60N
488. NCEI v4 Land + Ocean 90S-00N
489. NCEI v4 Land + Ocean 90S-20S
490. NCEI v4 Land 30S-00N
491. NCEI v4 Land + Ocean 90S-60S
492. NCEI v4 Land + Ocean 90S-90N
493. NCEI v4 Ocean 00N-30N
494. NCEI v4 Ocean 00N-90N
495. NCEI v4 Ocean 20N-90N
496. NCEI v4 Ocean 20S-20N
497. NCEI v4 Ocean 30N-60N
498. NCEI v4 Ocean 30S-00N
499. NCEI v4 Ocean 60N-90N
500. NCEI v4 Ocean 60S-30S
501. NCEI v4 Ocean 60S-60N
502. NCEI v4 Ocean 90S-00N
503. NCEI v4 Ocean 90S-20S
504. NCEI v4 Ocean 90S-60S
505. NCEI v4 Ocean 90S-90N
506. NCEI v4 Land 60S-30S_uncertainty
507. NCEI v4 Land 60N-90N_uncertainty
508. NCEI v4 Land 60S-60N_uncertainty
509. NCEI v4 Land 90S-00N_uncertainty
510. NCEI v4 Land 90S-20S_uncertainty
511. NCEI v4 Land 90S-60S_uncertainty
512. NCEI v4 Land 90S-90N_uncertainty
513. NCEI v4 Land + Ocean 00N-90N_uncertainty
514. NCEI v4 Land + Ocean 00N-30N_uncertainty
515. NCEI v4 Land + Ocean 20N-90N_uncertainty
516. NCEI v4 Land + Ocean 20S-20N_uncertainty
517. NCEI v4 Land + Ocean 30N-60N_uncertainty
518. NCEI v4 Land + Ocean 30S-00N_uncertainty
519. NCEI v4 Land 00N-30N_uncertainty
520. NCEI v4 Land 00N-90N_uncertainty
521. NCEI v4 Land 20N-90N_uncertainty
522. NCEI v4 Land 20S-20N_uncertainty
523. NCEI v4 Land 30N-60N_uncertainty
524. NCEI v4 Land + Ocean 60N-90N_uncertainty
525. NCEI v4 Land + Ocean 60S-30S_uncertainty
526. NCEI v4 Land + Ocean 60S-60N_uncertainty
527. NCEI v4 Land + Ocean 90S-00N_uncertainty
528. NCEI v4 Land + Ocean 90S-20S_uncertainty
529. NCEI v4 Land 30S-00N_uncertainty
530. NCEI v4 Land + Ocean 90S-60S_uncertainty
531. NCEI v4 Land + Ocean 90S-90N_uncertainty
532. NCEI v4 Ocean 00N-30N_uncertainty
533. NCEI v4 Ocean 00N-90N_uncertainty
534. NCEI v4 Ocean 20N-90N_uncertainty
535. NCEI v4 Ocean 20S-20N_uncertainty
536. NCEI v4 Ocean 30N-60N_uncertainty
537. NCEI v4 Ocean 30S-00N_uncertainty
538. NCEI v4 Ocean 60N-90N_uncertainty
539. NCEI v4 Ocean 60S-30S_uncertainty
540. NCEI v4 Ocean 60S-60N_uncertainty
541. NCEI v4 Ocean 90S-00N_uncertainty
542. NCEI v4 Ocean 90S-20S_uncertainty
543. NCEI v4 Ocean 90S-60S_uncertainty
544. NCEI v4 Ocean 90S-90N_uncertainty
545. NCEP/CSFR Surface Air Global
546. NCEP/CSFR Surface Air Global Land
547. NCEP/CSFR Surface Air Global Ocean
548. NCEP/CSFR Surface Air NH
549. NCEP/CSFR Surface Air NH Land
550. NCEP/CSFR Surface Air NH Ocean
551. NCEP/CSFR Surface Air NH Polar
552. NCEP/CSFR Surface Air NH Polar Land
553. NCEP/CSFR Surface Air NH Polar Ocean
554. NCEP/CSFR Surface Air SH
555. NCEP/CSFR Surface Air SH Land
556. NCEP/CSFR Surface Air SH Ocean
557. NCEP/CSFR Surface Air SH Polar
558. NCEP/CSFR Surface Air SH Polar Land
559. NCEP/CSFR Surface Air SH Polar Ocean
560. NCEP/CSFR Surface Air Tropics
561. NCEP/CSFR Surface Air Tropics Land
562. NCEP/CSFR Surface Air Tropics Ocean
563. NCEP/CSFR Surface Air USA 48
564. NCEP/CSFR Surface Air USA 48 Land
565. NCEP/CSFR Surface Air USA 48 Ocean
566. NCEP/DOE R2 Sea Surface Global
567. NCEP/DOE R2 Sea Surface NH
568. NCEP/DOE R2 Sea Surface SH
569. NCEP/DOE R2 Surface Air Global
570. NCEP/DOE R2 Surface Air Global Land
571. NCEP/DOE R2 Surface Air Global Ocean
572. NCEP/DOE R2 Surface Air NH
573. NCEP/DOE R2 Surface Air NH Land
574. NCEP/DOE R2 Surface Air NH Ocean
575. NCEP/DOE R2 Surface Air NH Polar
576. NCEP/DOE R2 Surface Air NH Polar Land
577. NCEP/DOE R2 Surface Air NH Polar Ocean
578. NCEP/DOE R2 Surface Air SH
579. NCEP/DOE R2 Surface Air SH Land
580. NCEP/DOE R2 Surface Air SH Ocean
581. NCEP/DOE R2 Surface Air SH Polar
582. NCEP/DOE R2 Surface Air SH Polar Land
583. NCEP/DOE R2 Surface Air SH Polar Ocean
584. NCEP/DOE R2 Surface Air Tropics
585. NCEP/DOE R2 Surface Air Tropics Land
586. NCEP/DOE R2 Surface Air Tropics Ocean
587. NCEP/DOE R2 Surface Air USA 48
588. NCEP/DOE R2 Surface Air USA 48 Land
589. NCEP/DOE R2 Surface Air USA 48 Ocean
590. NCEP/NCAR R1 Sea Surface Global
591. NCEP/NCAR R1 Sea Surface NH
592. NCEP/NCAR R1 Sea Surface SH
593. NCEP/NCAR R1 Surface Air Global
594. NCEP/NCAR R1 Surface Air Global Land
595. NCEP/NCAR R1 Surface Air Global Ocean
596. NCEP/NCAR R1 Surface Air NH
597. NCEP/NCAR R1 Surface Air NH Land
598. NCEP/NCAR R1 Surface Air NH Ocean
599. NCEP/NCAR R1 Surface Air NH Polar
600. NCEP/NCAR R1 Surface Air NH Polar Land
601. NCEP/NCAR R1 Surface Air NH Polar Ocean
602. NCEP/NCAR R1 Surface Air SH
603. NCEP/NCAR R1 Surface Air SH Land
604. NCEP/NCAR R1 Surface Air SH Ocean
605. NCEP/NCAR R1 Surface Air SH Polar
606. NCEP/NCAR R1 Surface Air SH Polar Land
607. NCEP/NCAR R1 Surface Air SH Polar Ocean
608. NCEP/NCAR R1 Surface Air Tropics
609. NCEP/NCAR R1 Surface Air Tropics Land
610. NCEP/NCAR R1 Surface Air Tropics Ocean
611. NCEP/NCAR R1 Surface Air USA 48
612. NCEP/NCAR R1 Surface Air USA 48 Land
613. NCEP/NCAR R1 Surface Air USA 48 Ocean
614. NOAA Global Mean Sea Level
615. NOAA Sunspot No.
616. NSIDC Sea Ice NH Extent
617. NSIDC Sea Ice NH Area
618. NSIDC Sea Ice SH Extent
619. NSIDC Sea Ice SH Area
620. NSIDC Sea Ice Global Extent
621. NSIDC Sea Ice Global Area
622. Ocean Mass Variation
623. Ocean Mass Variation_uncertainty
624. EUMETSAT OSI Barents Sea Sea Ice Area
625. EUMETSAT OSI Barents Sea Sea Ice Extent
626. EUMETSAT OSI Beaufort Sea Sea Ice Area
627. EUMETSAT OSI Beaufort Sea Sea Ice Extent
628. EUMETSAT OSI Amundsen-Bellingshausen Sea Ice Area
629. EUMETSAT OSI Amundsen-Bellingshausen Sea Ice Extent
630. EUMETSAT OSI Chukchi Sea Sea Ice Area
631. EUMETSAT OSI Chukchi Sea Sea Ice Extent
632. EUMETSAT OSI Dronning Maud Land Sea Ice Area
633. EUMETSAT OSI Dronning Maud Land Sea Ice Extent
634. EUMETSAT OSI East Siberian Sea Sea Ice Area
635. EUMETSAT OSI East Siberian Sea Sea Ice Extent
636. EUMETSAT OSI Fram Strait Sea Ice Area
637. EUMETSAT OSI Fram Strait Sea Ice Extent
638. EUMETSAT OSI Global Sea Ice Area
639. EUMETSAT OSI Global Sea Ice Extent
640. EUMETSAT OSI Indian Ocean Sea Ice Area
641. EUMETSAT OSI Indian Ocean Sea Ice Extent
642. EUMETSAT OSI Kara Sea Sea Ice Area
643. EUMETSAT OSI Kara Sea Sea Ice Extent
644. EUMETSAT OSI Laptev Sea Sea Ice Area
645. EUMETSAT OSI Laptev Sea Sea Ice Extent
646. EUMETSAT OSI Northern Barents Sea Sea Ice Area
647. EUMETSAT OSI Northern Barents Sea Sea Ice Extent
648. EUMETSAT OSI Northern Hemisphere Sea Ice Area
649. EUMETSAT OSI Northern Hemisphere Sea Ice Extent
650. EUMETSAT OSI Ross Sea Sea Ice Area
651. EUMETSAT OSI Ross Sea Sea Ice Extent
652. EUMETSAT OSI Southern Hemisphere Sea Ice Area
653. EUMETSAT OSI Southern Hemisphere Sea Ice Extent
654. EUMETSAT OSI Svalbard Sea Ice Area
655. EUMETSAT OSI Svalbard Sea Ice Extent
656. EUMETSAT OSI Troll Station Sea Ice Area
657. EUMETSAT OSI Troll Station Sea Ice Extent
658. EUMETSAT OSI Weddell Sea Sea Ice Area
659. EUMETSAT OSI Weddell Sea Sea Ice Extent
660. EUMETSAT OSI Western Pacific Ocean Sea Ice Area
661. EUMETSAT OSI Western Pacific Ocean Sea Ice Extent
662. OSIRIS Stratospheric Aerosol Optical Depth (550 nm) Global
663. OSIRIS Stratospheric Aerosol Optical Depth (550 nm) NH
664. OSIRIS Stratospheric Aerosol Optical Depth (550 nm) SH
665. PIOMAS Arctic Sea Ice Volume
666. PMOD TSI VIRGO A (orig.)
667. PMOD TSI VIRGO A+B (orig.)
668. PMOD TSI VIRGO A (new)
669. PMOD TSI VIRGO A+B (new)
670. PMOD TSI VIRGO A+B (orig.)_uncertainty
671. RATPAC-A Surface NH
672. RATPAC-A 850 mb NH
673. RATPAC-A 700 mb NH
674. RATPAC-A 500 mb NH
675. RATPAC-A 400 mb NH
676. RATPAC-A 300 mb NH
677. RATPAC-A 250 mb NH
678. RATPAC-A 200 mb NH
679. RATPAC-A 150 mb NH
680. RATPAC-A 100 mb NH
681. RATPAC-A 70 mb NH
682. RATPAC-A 50 mb NH
683. RATPAC-A 30 mb NH
684. RATPAC-A Surface SH
685. RATPAC-A 850 mb SH
686. RATPAC-A 700 mb SH
687. RATPAC-A 500 mb SH
688. RATPAC-A 400 mb SH
689. RATPAC-A 300 mb SH
690. RATPAC-A 250 mb SH
691. RATPAC-A 200 mb SH
692. RATPAC-A 150 mb SH
693. RATPAC-A 100 mb SH
694. RATPAC-A 70 mb SH
695. RATPAC-A 50 mb SH
696. RATPAC-A 30 mb SH
697. RATPAC-A Surface GLOBE
698. RATPAC-A 850 mb GLOBE
699. RATPAC-A 700 mb GLOBE
700. RATPAC-A 500 mb GLOBE
701. RATPAC-A 400 mb GLOBE
702. RATPAC-A 300 mb GLOBE
703. RATPAC-A 250 mb GLOBE
704. RATPAC-A 200 mb GLOBE
705. RATPAC-A 150 mb GLOBE
706. RATPAC-A 100 mb GLOBE
707. RATPAC-A 70 mb GLOBE
708. RATPAC-A 50 mb GLOBE
709. RATPAC-A 30 mb GLOBE
710. RATPAC-A Surface TROPICS (30S-30N)
711. RATPAC-A 850 mb TROPICS (30S-30N)
712. RATPAC-A 700 mb TROPICS (30S-30N)
713. RATPAC-A 500 mb TROPICS (30S-30N)
714. RATPAC-A 400 mb TROPICS (30S-30N)
715. RATPAC-A 300 mb TROPICS (30S-30N)
716. RATPAC-A 250 mb TROPICS (30S-30N)
717. RATPAC-A 200 mb TROPICS (30S-30N)
718. RATPAC-A 150 mb TROPICS (30S-30N)
719. RATPAC-A 100 mb TROPICS (30S-30N)
720. RATPAC-A 70 mb TROPICS (30S-30N)
721. RATPAC-A 50 mb TROPICS (30S-30N)
722. RATPAC-A 30 mb TROPICS (30S-30N)
723. RATPAC-A Surface NH Extratropics
724. RATPAC-A 850 mb NH Extratropics
725. RATPAC-A 700 mb NH Extratropics
726. RATPAC-A 500 mb NH Extratropics
727. RATPAC-A 400 mb NH Extratropics
728. RATPAC-A 300 mb NH Extratropics
729. RATPAC-A 250 mb NH Extratropics
730. RATPAC-A 200 mb NH Extratropics
731. RATPAC-A 150 mb NH Extratropics
732. RATPAC-A 100 mb NH Extratropics
733. RATPAC-A 70 mb NH Extratropics
734. RATPAC-A 50 mb NH Extratropics
735. RATPAC-A 30 mb NH Extratropics
736. RATPAC-A Surface SH Extratropics
737. RATPAC-A 850 mb SH Extratropics
738. RATPAC-A 700 mb SH Extratropics
739. RATPAC-A 500 mb SH Extratropics
740. RATPAC-A 400 mb SH Extratropics
741. RATPAC-A 300 mb SH Extratropics
742. RATPAC-A 250 mb SH Extratropics
743. RATPAC-A 200 mb SH Extratropics
744. RATPAC-A 150 mb SH Extratropics
745. RATPAC-A 100 mb SH Extratropics
746. RATPAC-A 70 mb SH Extratropics
747. RATPAC-A 50 mb SH Extratropics
748. RATPAC-A 30 mb SH Extratropics
749. RATPAC-A Surface TROPICS (20S-20N)
750. RATPAC-A 850 mb TROPICS (20S-20N)
751. RATPAC-A 700 mb TROPICS (20S-20N)
752. RATPAC-A 500 mb TROPICS (20S-20N)
753. RATPAC-A 400 mb TROPICS (20S-20N)
754. RATPAC-A 300 mb TROPICS (20S-20N)
755. RATPAC-A 250 mb TROPICS (20S-20N)
756. RATPAC-A 200 mb TROPICS (20S-20N)
757. RATPAC-A 150 mb TROPICS (20S-20N)
758. RATPAC-A 100 mb TROPICS (20S-20N)
759. RATPAC-A 70 mb TROPICS (20S-20N)
760. RATPAC-A 50 mb TROPICS (20S-20N)
761. RATPAC-A 30 mb TROPICS (20S-20N)
762. RATPAC-A 850-300 mb NH
763. RATPAC-A 850-300 mb SH
764. RATPAC-A 850-300 mb Global
765. RATPAC-A 850-300 mb Tropics
766. RATPAC-A 850-300 mb NH Extratropics
767. RATPAC-A 850-300 mb SH Extratropics
768. RATPAC-A 850-300 mb 20N-S
769. RATPAC-A 300-100 mb NH
770. RATPAC-A 300-100 mb SH
771. RATPAC-A 300-100 mb Global
772. RATPAC-A 300-100 mb Tropics
773. RATPAC-A 300-100 mb NH Extratropics
774. RATPAC-A 300-100 mb SH Extratropics
775. RATPAC-A 300-100 mb 20N-S
776. RATPAC-A 100-50 mb NH
777. RATPAC-A 100-50 mb SH
778. RATPAC-A 100-50 mb Global
779. RATPAC-A 100-50 mb Tropics
780. RATPAC-A 100-50 mb NH Extratropics
781. RATPAC-A 100-50 mb SH Extratropics
782. RATPAC-A 100-50 mb 20N-S
783. RSS TLS 3.3 -82.5/82.5
784. RSS TLS 3.3 -20.0/20.0
785. RSS TLS 3.3 20.0/82.5
786. RSS TLS 3.3 -82.5/-20.0
787. RSS TLS 3.3 60.0/82.5
788. RSS TLS 3.3 -82.5/-60.0
789. RSS TLS 3.3 Cont. USA
790. RSS TLS 3.3 0.0/82.5
791. RSS TLS 3.3 -82.5/0.0
792. RSS TLS 3.3 Land -82.5/82.5
793. RSS TLS 3.3 Land -20.0/20.0
794. RSS TLS 3.3 Land 20.0/82.5
795. RSS TLS 3.3 Land -82.5/-20.0
796. RSS TLS 3.3 Land 60.0/82.5
797. RSS TLS 3.3 Land -82.5/-60.0
798. RSS TLS 3.3 Ocean -82.5/82.5
799. RSS TLS 3.3 Ocean -20.0/20.0
800. RSS TLS 3.3 Ocean 20.0/82.5
801. RSS TLS 3.3 Ocean -82.5/-20.0
802. RSS TLS 3.3 Ocean 60.0/82.5
803. RSS TLS 3.3 Ocean -82.5/-60.0
804. RSS TLS 4.0 -82.5/82.5
805. RSS TLS 4.0 -20.0/20.0
806. RSS TLS 4.0 20.0/82.5
807. RSS TLS 4.0 -82.5/-20.0
808. RSS TLS 4.0 60.0/82.5
809. RSS TLS 4.0 -82.5/-60.0
810. RSS TLS 4.0 Cont. USA
811. RSS TLS 4.0 0.0/82.5
812. RSS TLS 4.0 -82.5/0.0
813. RSS TLS 4.0 Land -82.5/82.5
814. RSS TLS 4.0 Land -20.0/20.0
815. RSS TLS 4.0 Land 20.0/82.5
816. RSS TLS 4.0 Land -82.5/-20.0
817. RSS TLS 4.0 Land 60.0/82.5
818. RSS TLS 4.0 Land -82.5/-60.0
819. RSS TLS 4.0 Ocean -82.5/82.5
820. RSS TLS 4.0 Ocean -20.0/20.0
821. RSS TLS 4.0 Ocean 20.0/82.5
822. RSS TLS 4.0 Ocean -82.5/-20.0
823. RSS TLS 4.0 Ocean 60.0/82.5
824. RSS TLS 4.0 Ocean -82.5/-60.0
825. RSS TLT 3.3 -70.0/82.5
826. RSS TLT 3.3 -20.0/20.0
827. RSS TLT 3.3 20.0/82.5
828. RSS TLT 3.3 -70.0/-20.0
829. RSS TLT 3.3 60.0/82.5
830. RSS TLT 3.3 -70.0/-60.0
831. RSS TLT 3.3 Cont. USA
832. RSS TLT 3.3 0.0/82.5
833. RSS TLT 3.3 -70.0/0.0
834. RSS TLT 3.3 Land -70.0/82.5
835. RSS TLT 3.3 Land -20.0/20.0
836. RSS TLT 3.3 Land 20.0/82.5
837. RSS TLT 3.3 Land -70.0/-20.0
838. RSS TLT 3.3 Land 60.0/82.5
839. RSS TLT 3.3 Land -70.0/-60.0
840. RSS TLT 3.3 Ocean -70.0/82.5
841. RSS TLT 3.3 Ocean -20.0/20.0
842. RSS TLT 3.3 Ocean 20.0/82.5
843. RSS TLT 3.3 Ocean -70.0/-20.0
844. RSS TLT 3.3 Ocean 60.0/82.5
845. RSS TLT 3.3 Ocean -70.0/-60.0
846. RSS TLT 4.0 -70.0/82.5
847. RSS TLT 4.0 -20.0/20.0
848. RSS TLT 4.0 20.0/82.5
849. RSS TLT 4.0 -70.0/-20.0
850. RSS TLT 4.0 60.0/82.5
851. RSS TLT 4.0 -70.0/-60.0
852. RSS TLT 4.0 Cont. USA
853. RSS TLT 4.0 0.0/82.5
854. RSS TLT 4.0 -70.0/0.0
855. RSS TLT 4.0 Land -70.0/82.5
856. RSS TLT 4.0 Land -20.0/20.0
857. RSS TLT 4.0 Land 20.0/82.5
858. RSS TLT 4.0 Land -70.0/-20.0
859. RSS TLT 4.0 Land 60.0/82.5
860. RSS TLT 4.0 Land -70.0/-60.0
861. RSS TLT 4.0 Ocean -70.0/82.5
862. RSS TLT 4.0 Ocean -20.0/20.0
863. RSS TLT 4.0 Ocean 20.0/82.5
864. RSS TLT 4.0 Ocean -70.0/-20.0
865. RSS TLT 4.0 Ocean 60.0/82.5
866. RSS TLT 4.0 Ocean -70.0/-60.0
867. RSS TMT 3.3 -82.5/82.5
868. RSS TMT 3.3 -20.0/20.0
869. RSS TMT 3.3 20.0/82.5
870. RSS TMT 3.3 -82.5/-20.0
871. RSS TMT 3.3 60.0/82.5
872. RSS TMT 3.3 -82.5/-60.0
873. RSS TMT 3.3 Cont. USA
874. RSS TMT 3.3 0.0/82.5
875. RSS TMT 3.3 -82.5/0.0
876. RSS TMT 3.3 Land -82.5/82.5
877. RSS TMT 3.3 Land -20.0/20.0
878. RSS TMT 3.3 Land 20.0/82.5
879. RSS TMT 3.3 Land -82.5/-20.0
880. RSS TMT 3.3 Land 60.0/82.5
881. RSS TMT 3.3 Land -82.5/-60.0
882. RSS TMT 3.3 Ocean -82.5/82.5
883. RSS TMT 3.3 Ocean -20.0/20.0
884. RSS TMT 3.3 Ocean 20.0/82.5
885. RSS TMT 3.3 Ocean -82.5/-20.0
886. RSS TMT 3.3 Ocean 60.0/82.5
887. RSS TMT 3.3 Ocean -82.5/-60.0
888. RSS TMT 4.0 -82.5/82.5
889. RSS TMT 4.0 -20.0/20.0
890. RSS TMT 4.0 20.0/82.5
891. RSS TMT 4.0 -82.5/-20.0
892. RSS TMT 4.0 60.0/82.5
893. RSS TMT 4.0 -82.5/-60.0
894. RSS TMT 4.0 Cont. USA
895. RSS TMT 4.0 0.0/82.5
896. RSS TMT 4.0 -82.5/0.0
897. RSS TMT 4.0 Land -82.5/82.5
898. RSS TMT 4.0 Land -20.0/20.0
899. RSS TMT 4.0 Land 20.0/82.5
900. RSS TMT 4.0 Land -82.5/-20.0
901. RSS TMT 4.0 Land 60.0/82.5
902. RSS TMT 4.0 Land -82.5/-60.0
903. RSS TMT 4.0 Ocean -82.5/82.5
904. RSS TMT 4.0 Ocean -20.0/20.0
905. RSS TMT 4.0 Ocean 20.0/82.5
906. RSS TMT 4.0 Ocean -82.5/-20.0
907. RSS TMT 4.0 Ocean 60.0/82.5
908. RSS TMT 4.0 Ocean -82.5/-60.0
909. RSS TTS 3.3 -82.5/82.5
910. RSS TTS 3.3 -20.0/20.0
911. RSS TTS 3.3 20.0/82.5
912. RSS TTS 3.3 -82.5/-20.0
913. RSS TTS 3.3 60.0/82.5
914. RSS TTS 3.3 -82.5/-60.0
915. RSS TTS 3.3 Cont. USA
916. RSS TTS 3.3 0.0/82.5
917. RSS TTS 3.3 -82.5/0.0
918. RSS TTS 3.3 Land -82.5/82.5
919. RSS TTS 3.3 Land -20.0/20.0
920. RSS TTS 3.3 Land 20.0/82.5
921. RSS TTS 3.3 Land -82.5/-20.0
922. RSS TTS 3.3 Land 60.0/82.5
923. RSS TTS 3.3 Land -82.5/-60.0
924. RSS TTS 3.3 Ocean -82.5/82.5
925. RSS TTS 3.3 Ocean -20.0/20.0
926. RSS TTS 3.3 Ocean 20.0/82.5
927. RSS TTS 3.3 Ocean -82.5/-20.0
928. RSS TTS 3.3 Ocean 60.0/82.5
929. RSS TTS 3.3 Ocean -82.5/-60.0
930. RSS TTS 4.0 -82.5/82.5
931. RSS TTS 4.0 -20.0/20.0
932. RSS TTS 4.0 20.0/82.5
933. RSS TTS 4.0 -82.5/-20.0
934. RSS TTS 4.0 60.0/82.5
935. RSS TTS 4.0 -82.5/-60.0
936. RSS TTS 4.0 Cont. USA
937. RSS TTS 4.0 0.0/82.5
938. RSS TTS 4.0 -82.5/0.0
939. RSS TTS 4.0 Land -82.5/82.5
940. RSS TTS 4.0 Land -20.0/20.0
941. RSS TTS 4.0 Land 20.0/82.5
942. RSS TTS 4.0 Land -82.5/-20.0
943. RSS TTS 4.0 Land 60.0/82.5
944. RSS TTS 4.0 Land -82.5/-60.0
945. RSS TTS 4.0 Ocean -82.5/82.5
946. RSS TTS 4.0 Ocean -20.0/20.0
947. RSS TTS 4.0 Ocean 20.0/82.5
948. RSS TTS 4.0 Ocean -82.5/-20.0
949. RSS TTS 4.0 Ocean 60.0/82.5
950. RSS TTS 4.0 Ocean -82.5/-60.0
951. RSS TTT 3.3 -82.5/82.5
952. RSS TTT 3.3 -20.0/20.0
953. RSS TTT 3.3 20.0/82.5
954. RSS TTT 3.3 -82.5/-20.0
955. RSS TTT 3.3 60.0/82.5
956. RSS TTT 3.3 -82.5/-60.0
957. RSS TTT 3.3 Cont. USA
958. RSS TTT 3.3 0.0/82.5
959. RSS TTT 3.3 -82.5/0.0
960. RSS TTT 3.3 Land -82.5/82.5
961. RSS TTT 3.3 Land -20.0/20.0
962. RSS TTT 3.3 Land 20.0/82.5
963. RSS TTT 3.3 Land -82.5/-20.0
964. RSS TTT 3.3 Land 60.0/82.5
965. RSS TTT 3.3 Land -82.5/-60.0
966. RSS TTT 3.3 Ocean -82.5/82.5
967. RSS TTT 3.3 Ocean -20.0/20.0
968. RSS TTT 3.3 Ocean 20.0/82.5
969. RSS TTT 3.3 Ocean -82.5/-20.0
970. RSS TTT 3.3 Ocean 60.0/82.5
971. RSS TTT 3.3 Ocean -82.5/-60.0
972. RSS TTT 4.0 -82.5/82.5
973. RSS TTT 4.0 -20.0/20.0
974. RSS TTT 4.0 20.0/82.5
975. RSS TTT 4.0 -82.5/-20.0
976. RSS TTT 4.0 60.0/82.5
977. RSS TTT 4.0 -82.5/-60.0
978. RSS TTT 4.0 Cont. USA
979. RSS TTT 4.0 0.0/82.5
980. RSS TTT 4.0 -82.5/0.0
981. RSS TTT 4.0 Land -82.5/82.5
982. RSS TTT 4.0 Land -20.0/20.0
983. RSS TTT 4.0 Land 20.0/82.5
984. RSS TTT 4.0 Land -82.5/-20.0
985. RSS TTT 4.0 Land 60.0/82.5
986. RSS TTT 4.0 Land -82.5/-60.0
987. RSS TTT 4.0 Ocean -82.5/82.5
988. RSS TTT 4.0 Ocean -20.0/20.0
989. RSS TTT 4.0 Ocean 20.0/82.5
990. RSS TTT 4.0 Ocean -82.5/-20.0
991. RSS TTT 4.0 Ocean 60.0/82.5
992. RSS TTT 4.0 Ocean -82.5/-60.0
993. Rutgers Eurasia Snow Cover
994. Rutgers N. America (No Greenland) Snow Cover
995. Rutgers N. America Snow Cover
996. Rutgers NH Snow Cover
997. STAR v5.0 TLS Global Mean
998. STAR v5.0 TLS NH
999. STAR v5.0 TLS SH
1000. STAR v5.0 TLS Global Land
1001. STAR v5.0 TLS Global Ocean
1002. STAR v5.0 TLT Global Mean
1003. STAR v5.0 TLT NH
1004. STAR v5.0 TLT SH
1005. STAR v5.0 TLT Global Land
1006. STAR v5.0 TLT Global Ocean
1007. STAR v5.0 TMT Global Mean
1008. STAR v5.0 TMT NH
1009. STAR v5.0 TMT SH
1010. STAR v5.0 TMT Global Land
1011. STAR v5.0 TMT Global Ocean
1012. STAR v5.0 TUT Global Mean
1013. STAR v5.0 TUT NH
1014. STAR v5.0 TUT SH
1015. STAR v5.0 TUT Global Land
1016. STAR v5.0 TUT Global Ocean
1017. STAR v5.0 TTT Global Mean
1018. STAR v5.0 TTT NH
1019. STAR v5.0 TTT SH
1020. STAR v5.0 TTT Global Land
1021. STAR v5.0 TTT Global Ocean
1022. TSI Reconstructed
1023. UAH TLS 5.6 Global
1024. UAH TLS 5.6 Global Land
1025. UAH TLS 5.6 Global Ocean
1026. UAH TLS 5.6 NH
1027. UAH TLS 5.6 NH Land
1028. UAH TLS 5.6 NH Ocean
1029. UAH TLS 5.6 SH
1030. UAH TLS 5.6 SH Land
1031. UAH TLS 5.6 SH Ocean
1032. UAH TLS 5.6 Tropics
1033. UAH TLS 5.6 Tropics Land
1034. UAH TLS 5.6 Tropics Ocean
1035. UAH TLS 5.6 NH Extratropics
1036. UAH TLS 5.6 NH Extratropics Land
1037. UAH TLS 5.6 NH Extratropics Ocean
1038. UAH TLS 5.6 SH Extratropics
1039. UAH TLS 5.6 SH Extratropics Land
1040. UAH TLS 5.6 SH Extratropics Ocean
1041. UAH TLS 5.6 NH Polar
1042. UAH TLS 5.6 NH Polar Land
1043. UAH TLS 5.6 NH Polar Ocean
1044. UAH TLS 5.6 SH Polar
1045. UAH TLS 5.6 SH Polar Land
1046. UAH TLS 5.6 SH Polar Ocean
1047. UAH TLS 5.6 USA 48
1048. UAH TLS 5.6 USA 48 + Alaska
1049. UAH TLS 5.6 Australia
1050. UAH TLS 6.0 Global
1051. UAH TLS 6.0 Global Land
1052. UAH TLS 6.0 Global Ocean
1053. UAH TLS 6.0 NH
1054. UAH TLS 6.0 NH Land
1055. UAH TLS 6.0 NH Ocean
1056. UAH TLS 6.0 SH
1057. UAH TLS 6.0 SH Land
1058. UAH TLS 6.0 SH Ocean
1059. UAH TLS 6.0 Tropics
1060. UAH TLS 6.0 Tropics Land
1061. UAH TLS 6.0 Tropics Ocean
1062. UAH TLS 6.0 NH Extratropics
1063. UAH TLS 6.0 NH Extratropics Land
1064. UAH TLS 6.0 NH Extratropics Ocean
1065. UAH TLS 6.0 SH Extratropics
1066. UAH TLS 6.0 SH Extratropics Land
1067. UAH TLS 6.0 SH Extratropics Ocean
1068. UAH TLS 6.0 NH Polar
1069. UAH TLS 6.0 NH Polar Land
1070. UAH TLS 6.0 NH Polar Ocean
1071. UAH TLS 6.0 SH Polar
1072. UAH TLS 6.0 SH Polar Land
1073. UAH TLS 6.0 SH Polar Ocean
1074. UAH TLS 6.0 USA 48
1075. UAH TLS 6.0 USA 48 + Alaska
1076. UAH TLS 6.0 Australia
1077. UAH TLT 5.6 Global
1078. UAH TLT 5.6 Global Land
1079. UAH TLT 5.6 Global Ocean
1080. UAH TLT 5.6 NH
1081. UAH TLT 5.6 NH Land
1082. UAH TLT 5.6 NH Ocean
1083. UAH TLT 5.6 SH
1084. UAH TLT 5.6 SH Land
1085. UAH TLT 5.6 SH Ocean
1086. UAH TLT 5.6 Tropics
1087. UAH TLT 5.6 Tropics Land
1088. UAH TLT 5.6 Tropics Ocean
1089. UAH TLT 5.6 NH Extratropics
1090. UAH TLT 5.6 NH Extratropics Land
1091. UAH TLT 5.6 NH Extratropics Ocean
1092. UAH TLT 5.6 SH Extratropics
1093. UAH TLT 5.6 SH Extratropics Land
1094. UAH TLT 5.6 SH Extratropics Ocean
1095. UAH TLT 5.6 NH Polar
1096. UAH TLT 5.6 NH Polar Land
1097. UAH TLT 5.6 NH Polar Ocean
1098. UAH TLT 5.6 SH Polar
1099. UAH TLT 5.6 SH Polar Land
1100. UAH TLT 5.6 SH Polar Ocean
1101. UAH TLT 5.6 USA 48
1102. UAH TLT 5.6 USA 48 + Alaska
1103. UAH TLT 5.6 Australia
1104. UAH TLT 6.0 Global
1105. UAH TLT 6.0 Global Land
1106. UAH TLT 6.0 Global Ocean
1107. UAH TLT 6.0 NH
1108. UAH TLT 6.0 NH Land
1109. UAH TLT 6.0 NH Ocean
1110. UAH TLT 6.0 SH
1111. UAH TLT 6.0 SH Land
1112. UAH TLT 6.0 SH Ocean
1113. UAH TLT 6.0 Tropics
1114. UAH TLT 6.0 Tropics Land
1115. UAH TLT 6.0 Tropics Ocean
1116. UAH TLT 6.0 NH Extratropics
1117. UAH TLT 6.0 NH Extratropics Land
1118. UAH TLT 6.0 NH Extratropics Ocean
1119. UAH TLT 6.0 SH Extratropics
1120. UAH TLT 6.0 SH Extratropics Land
1121. UAH TLT 6.0 SH Extratropics Ocean
1122. UAH TLT 6.0 NH Polar
1123. UAH TLT 6.0 NH Polar Land
1124. UAH TLT 6.0 NH Polar Ocean
1125. UAH TLT 6.0 SH Polar
1126. UAH TLT 6.0 SH Polar Land
1127. UAH TLT 6.0 SH Polar Ocean
1128. UAH TLT 6.0 USA 48
1129. UAH TLT 6.0 USA 48 + Alaska
1130. UAH TLT 6.0 Australia
1131. UAH TMT 5.6 Global
1132. UAH TMT 5.6 Global Land
1133. UAH TMT 5.6 Global Ocean
1134. UAH TMT 5.6 NH
1135. UAH TMT 5.6 NH Land
1136. UAH TMT 5.6 NH Ocean
1137. UAH TMT 5.6 SH
1138. UAH TMT 5.6 SH Land
1139. UAH TMT 5.6 SH Ocean
1140. UAH TMT 5.6 Tropics
1141. UAH TMT 5.6 Tropics Land
1142. UAH TMT 5.6 Tropics Ocean
1143. UAH TMT 5.6 NH Extratropics
1144. UAH TMT 5.6 NH Extratropics Land
1145. UAH TMT 5.6 NH Extratropics Ocean
1146. UAH TMT 5.6 SH Extratropics
1147. UAH TMT 5.6 SH Extratropics Land
1148. UAH TMT 5.6 SH Extratropics Ocean
1149. UAH TMT 5.6 NH Polar
1150. UAH TMT 5.6 NH Polar Land
1151. UAH TMT 5.6 NH Polar Ocean
1152. UAH TMT 5.6 SH Polar
1153. UAH TMT 5.6 SH Polar Land
1154. UAH TMT 5.6 SH Polar Ocean
1155. UAH TMT 5.6 USA 48
1156. UAH TMT 5.6 USA 48 + Alaska
1157. UAH TMT 5.6 Australia
1158. UAH TMT 6.0 Global
1159. UAH TMT 6.0 Global Land
1160. UAH TMT 6.0 Global Ocean
1161. UAH TMT 6.0 NH
1162. UAH TMT 6.0 NH Land
1163. UAH TMT 6.0 NH Ocean
1164. UAH TMT 6.0 SH
1165. UAH TMT 6.0 SH Land
1166. UAH TMT 6.0 SH Ocean
1167. UAH TMT 6.0 Tropics
1168. UAH TMT 6.0 Tropics Land
1169. UAH TMT 6.0 Tropics Ocean
1170. UAH TMT 6.0 NH Extratropics
1171. UAH TMT 6.0 NH Extratropics Land
1172. UAH TMT 6.0 NH Extratropics Ocean
1173. UAH TMT 6.0 SH Extratropics
1174. UAH TMT 6.0 SH Extratropics Land
1175. UAH TMT 6.0 SH Extratropics Ocean
1176. UAH TMT 6.0 NH Polar
1177. UAH TMT 6.0 NH Polar Land
1178. UAH TMT 6.0 NH Polar Ocean
1179. UAH TMT 6.0 SH Polar
1180. UAH TMT 6.0 SH Polar Land
1181. UAH TMT 6.0 SH Polar Ocean
1182. UAH TMT 6.0 USA 48
1183. UAH TMT 6.0 USA 48 + Alaska
1184. UAH TMT 6.0 Australia
1185. UAH TTP 6.0 Global
1186. UAH TTP 6.0 Global Land
1187. UAH TTP 6.0 Global Ocean
1188. UAH TTP 6.0 NH
1189. UAH TTP 6.0 NH Land
1190. UAH TTP 6.0 NH Ocean
1191. UAH TTP 6.0 SH
1192. UAH TTP 6.0 SH Land
1193. UAH TTP 6.0 SH Ocean
1194. UAH TTP 6.0 Tropics
1195. UAH TTP 6.0 Tropics Land
1196. UAH TTP 6.0 Tropics Ocean
1197. UAH TTP 6.0 NH Extratropics
1198. UAH TTP 6.0 NH Extratropics Land
1199. UAH TTP 6.0 NH Extratropics Ocean
1200. UAH TTP 6.0 SH Extratropics
1201. UAH TTP 6.0 SH Extratropics Land
1202. UAH TTP 6.0 SH Extratropics Ocean
1203. UAH TTP 6.0 NH Polar
1204. UAH TTP 6.0 NH Polar Land
1205. UAH TTP 6.0 NH Polar Ocean
1206. UAH TTP 6.0 SH Polar
1207. UAH TTP 6.0 SH Polar Land
1208. UAH TTP 6.0 SH Polar Ocean
1209. UAH TTP 6.0 USA 48
1210. UAH TTP 6.0 USA 48 + Alaska
1211. UAH TTP 6.0 Australia