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Time series of snowfall observations from over 500 stations in Oregon (OR) and Washington (WA) were generated for subregions of these states. Data problems encountered were as follows: 1) monthly totals in printed reports prior to 1940 that were not in the digital archive, 2) archived data listed as “missing” that were available, 3) digitized reports after 2010 eliminated good data, and 4) “zero” totals incorrectly entered in the official archive rather than “missing,” especially after 1980. Though addressing these was done, there is reduced confidence that some regional time series are representative of true long-term trends, especially for regions with few systematically reporting stations. For most regions characterized by consistent monitoring and with the most robust statistical reproducibility, we find no statistically significant trends in their periods of record (up to 131 years) for November–April seasonal totals through April 2020. This result includes the main snowfall regions of the Cascade Range. However, snowfall in some lower-elevation areas of OR and WA appear to have experienced declining trends, consistent with an increase in northeastern Pacific Ocean temperatures. Finally, previously constructed time series through April 2011 for regions in California are updated through April 2020 to include the recent, exceptionally low seasonal totals on the western slopes of the Sierra Nevada. This update indicates 2014/15 was the record lowest, 2013/14 was the 5th lowest, and 2012/13 was the 14th lowest of 142 years. Even so, the 1879–2020 linear trend in this key watershed region, though −2.6% decade−1, was not significantly different from zero due to high interannual variability and reconstruction uncertainty.

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A 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a wide range of three model free parameters are swept to produce fits to a range of observed surface temperature trends from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived effective climate sensitivities (EffCS) are larger than those over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.94 deg. C global EffCS) than the older Representative Concentration Pathway forcing (RCP6, 2.60 deg. C global average EffCS). Diagnosed EffCS increases with increasing ocean or land heat storage, with close to 1 deg. C EffCS increase associated with ocean heat storage, but only 0.13 deg. C increase from land heat storage. The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone.

Global Climate is one chapter from the State of the Climate in 2019 annual report and is avail- able from https://doi.org/10.1175/BAMS-D-20-0104.1 Compiled by NOAA’s National Centers for Environmental Information, State of the Climate in 2019 is based on contributions from scien- tists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instru- ments located on land, water, ice, and in space. The full report is available from https://doi.org/10.1175/2020BAMSStateoftheClimate.1.

Observed rates of global-average deep-ocean and surface warming during 1880–2020 are matched with a 1D forcing-feedback model of vertical energy flow departures from assumed energy equilibrium driven by both anthropogenic and natural forcings. The monthly time resolution model ocean has a mixed layer, a second layer to 2,000 m depth, and a third layer extending to the ocean bottom. The model mixed layer temperature is radiatively forced with estimates of anthropogenic, volcanic, and direct solar forcings since 1765, and radiatively and non-radiatively forced with the observed history of El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) activity since 1880. Model adjustable parameters are chosen to match observed sea surface temperature (SST) and deep ocean (0-2000 m) temperature trends during 1960–2020, as well as twenty years of lag regression relationships between sea surface temperature and satellite longwave and shortwave flux anomalies at the top of the atmosphere. The results support the dominant role of anthropogenic greenhouse gas forcing in ocean warming since 1880. Without ENSO and PDO effects, diagnosed climate sensitivity is 1.8 and 2.2 deg. C for two sea surface temperature datasets, respectively. Inclusion of ENSO and PDO improves agreement between model and observations, from 81–92% explained variance, and reduces the diagnosed sensitivity to 1.5 to 1.8 deg. C, depending on the surface temperature dataset. The global warming slowdown during 1998–2012 is also well matched after inclusion of ENSO and PDO effects.

Temperature readings observed at surface weather stations have been used for detecting changes in climate due to their long period of observations. The most common temperature metrics recorded are the daily maximum (TMax) and minimum (TMin) extremes. Unfortunately, influences besides background climate variations impact these measurements such as changes in (1) instruments, (2) location, (3) time of observation, and (4) the surrounding artifacts of human civilization (buildings, farms, streets, etc.) Quantifying (4) is difficult because the surrounding infrastructure, unique to each site, often changes slowly and variably and is thus resistant to general algorithms for adjustment. We explore a direct method of detecting this impact by comparing a single station that experienced significant development from 1895 to 2019, and especially since 1970, relative to several other stations with lesser degrees of such development (after adjustments for the (1) to (3) are applied). The target station is Fresno, California (metro population ~ 15,000 in 1900 and ~ 1 million in 2019) situated on the eastern side of the broad, flat San Joaquin Valley in which several other stations reside. A unique component of this study is the use of pentad (5-day averages) as the test metric. Results indicate that Fresno experienced + 0.4 °C decade−1 more nighttime warming (TMin) since 1970 than its neighbors—a time when population grew almost 300%. There was little difference seen in TMax trends between Fresno and non-Fresno stations since 1895 with TMax trends being near zero. A case is made for the use of TMax as the preferred climate metric relative to TMin for a variety of physical reasons. Additionally, temperatures measured at systematic times of the day (i.e., hourly) show promise as climate indicators as compared with TMax and especially TMin (and thus TAvg) due to several complicating factors involved with daily high and low measurements.

The Intergovernmental Panel on Climate Change Assessment Report 5 (IPCC AR5, 2013) discussed bulk atmospheric temperatures as indicators of climate variability and change. We examine four satellite...

We identify and remove the main natural perturbations (e.g. volcanic activity, ENSOs) from the global mean lower tropospheric temperatures (T LT ) over January 1979 - June 2017 to estimate the underlying, potentially human-forced trend. The unaltered value is +0.155 K dec−1 while the adjusted trend is +0.096 K dec−1, related primarily to the removal of volcanic cooling in the early part of the record. This is essentially the same value we determined in 1994 (+0.09 K dec−1, Christy and McNider, 1994) using only 15 years of data. If the warming rate of +0.096 K dec−1 represents the net T LT response to increasing greenhouse radiative forcings, this implies that the T LT tropospheric transient climate response (ΔT LT at the time CO2 doubles) is +1.10 ± 0.26 K which is about half of the average of the IPCC AR5 climate models of 2.31 ± 0.20 K. Assuming that the net remaining unknown internal and external natural forcing over this period is near zero, the mismatch since 1979 between observations and CMIP-5 model values suggests that excessive sensitivity to enhanced radiative forcing in the models can be appreciable. The tropical region is mainly responsible for this discrepancy suggesting processes that are the likely sources of the extra sensitivity are (a) the parameterized hydrology of the deep atmosphere, (b) the parameterized heat-partitioning at the oceanatmosphere interface and/or (c) unknown natural variations.

Version 6 of the UAH MSU/AMSU global satellite temperature dataset represents an extensive revision of the procedures employed in previous versions of the UAH datasets. The two most significant results from an end-user perspective are (1) a decrease in the global-average lower tropospheric temperature (LT) trend from +0.14°C decade−1 to +0.11°C decade−1 (Jan. 1979 through Dec. 2015); and (2) the geographic distribution of the LT trends, including higher spatial resolution, owing to a new method for computing LT. We describe the major changes in processing strategy, including a new method for monthly gridpoint averaging which uses all of the footprint data yet eliminates the need for limb correction; a new multi-channel (rather than multi-angle) method for computing the lower tropospheric (LT) temperature product which requires an additional tropopause (TP) channel to be used; and a new empirical method for diurnal drift correction. We show results for LT, the midtroposphere (MT, from MSU2/AMSU5), and lower stratosphere (LS, from MSU4/AMSU9). A 0.03°C decade−1 reduction in the global LT trend from the Version 5.6 product is partly due to lesser sensitivity of the new LT to land surface skin temperature (est. 0.01°C decade−1), with the remainder of the reduction (0.02°C decade−1) due to the new diurnal drift adjustment, the more robust method of LT calculation, and other changes in processing procedures.

This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m−2 and thus a probable contribution to the “warming hole” over the Southeast during the majority of the twentieth century.

Three time series of average summer [June–August (JJA)] daily maximum temperature (TMax) are developed for three interior regions of Alabama from stations with varying periods of record and unknown inhomogeneities. The time frame is 1883–2014. Inhomogeneities for each station’s time series are determined from pairwise comparisons with no use of station metadata other than location. The time series for the three adjoining regions are constructed separately and are then combined as a whole assuming trends over 132 yr will have little spatial variation either intraregionally or interregionally for these spatial scales. Varying the parameters of the construction methodology creates 333 time series with a central trend value based on the largest group of stations of −0.07°C decade−1 with a best-guess estimate of measurement uncertainty from −0.12° to −0.02°C decade−1. This best-guess result is insignificantly different (0.01°C decade−1) from a similar regional calculation using NOAA’s divisional dataset based on daily data from the Global Historical Climatology Network (nClimDiv) beginning in 1895. Summer TMax is a better proxy, when compared with daily minimum temperature and thus daily average temperature, for the deeper tropospheric temperature (where the enhanced greenhouse signal is maximized) as a result of afternoon convective mixing. Thus, TMax more closely represents a critical climate parameter: atmospheric heat content. Comparison between JJA TMax and deep tropospheric temperature anomalies indicates modest agreement (r2 = 0.51) for interior Alabama while agreement for the conterminous United States as given by TMax from the nClimDiv dataset is much better (r2 = 0.86). Seventy-seven CMIP5 climate model runs are examined for Alabama and indicate no skill at replicating long-term temperature and precipitation changes since 1895.

Simulated stratospheric temperatures over the period 1979–2016 in models from the Chemistry-Climate Model Initiative are compared with recently updated and extended satellite data sets. The multimodel mean global temperature trends over 1979–2005 are -0.88 ± 0.23, -0.70 ± 0.16, and -0.50 ± 0.12 K/decade for the Stratospheric Sounding Unit (SSU) channels 3 (~40–50 km), 2 (~35–45 km), and 1 (~25–35 km), respectively (with 95% confidence intervals). These are within the uncertainty bounds of the observed temperature trends from two reprocessed SSU data sets. In the lower stratosphere, the multimodel mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13–22 km) is -0.25 ± 0.12 K/decade over 1979–2005, consistent with observed estimates from three versions of this satellite record. The models and an extended satellite data set comprised of SSU with the Advanced Microwave Sounding Unit-A show weaker global stratospheric cooling over 1998–2016 compared to the period of intensive ozone depletion (1979–1997). This is due to the reduction in ozone-induced cooling from the slowdown of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite-observed stratospheric temperature trends than was reported by Thompson et al. (2012, https://doi.org/10.1038/nature11579) for the previous versions of the SSU record and chemistry-climate models. The improved agreement mainly comes from updates to the satellite records; the range of stratospheric temperature trends over 1979–2005 simulated in Chemistry-Climate Model Initiative models is comparable to the previous generation of chemistry-climate models.

We estimate trends in US regional precipitation on multiple time spans and scales relevant to the detection of changes in climatic regimes. A large literature has shown that trend estimation in hydrological series may be affected by long-term persistence (LTP) and selection of sample length. We show that 2000-year proxy-based reconstructions of the Palmer Modified Drought Index for the US Southeast (SE) and Pacific Coast (PC) regions exhibit LTP and reveal post- 1900 changes to be within the range of longer-term natural fluctuations. We also use a new data base of daily precipitation records for 20 locations (10 PC and 10 SE) extending back in many cases to the 1870s. Over the 1901–2017 interval upward trends in some measures of average and extreme precipitation appear, but they are not consistently significant and in the full records back to 1872 they largely disappear. They also disappear or reverse in the post-1978 portion of the data set, which is inconsistent with them being responses to enhanced greenhouse gas forcing. We conclude that natural variability is likely the dominant driver of historical changes in precipitation and hence drought dynamics in the US SE and PC.

We analyze monthly tropical near surface air temperature and Mauna Loa Observatory carbon dioxide (CO2) data within 1960–2016 to identify different carbon cycle responses for two El Nino types: El Ninos originating in the central tropical Pacific (CP El Nino) and El Ninos originating in the eastern tropical Pacific (EP El Nino). We find significant differences between the two types of El Nino events with respect to time delay of the CO2 rise rate that follows the increase in tropical near surface air temperatures caused by El Nino events. The average time lag of the CP El Nino is 4.0 ± 1.7 months, while the mean time lag of EP El Nino is found to be 8.5 ± 2.3 months. The average lag of all considered 1960–2016 El Ninos is 5.2 ± 2.7 months. In contrast the sensitivity of the CO2 growth rate to tropical near surface air temperature increase is determined to be about the same for both El Nino types equal to 2.8 ± 0.9 ppm yr−1 K−1 (or 5.9 ± 1.9 GtC yr−1 K−1). Our results should be useful for the understanding of the carbon cycle and constraining it in climate models.

The International Surface Temperature Initiative is a worldwide effort to locate weather observations, digitize them for public access, and attach provenance to them. As part of that effort, this study sought documents of temperature observations for the nation of Uganda. Although scattered reports were found for the 1890s, consistent record keeping appears to have begun in 1900. Data were keyed in from images of several types of old forms as well as accessed electronically from several sources to extend the time series of 32 stations with at least 4 yr of data back as far as data were available. Important gaps still remain; 1979–93 has virtually no observations from any station. Because many stations were represented by more than one data source, a scheme is described to extract the “best guess” values for each station of monthly averages of the daily maximum, minimum, and mean temperature. A preliminary examination of the national time series indicates that, since the early twentieth century, it appears that Uganda experienced essentially no change in monthly-average daily maximum temperature but did experience a considerable rise in monthly-average daily minimum temperature, concentrated in the last three decades. Because there are many gaps in the data, it is hoped that readers with information on extant data that were not discovered for this study will contact the author or the project so that the data may be archived.

In 2000 a panel of the U.S. National Research Council in a report with the same title suggested, among other things, that a “substantial disparity remains” between the observed warming rates of the surface and troposphere. Also, in 2000, the climate models showed more warming of the tropical atmosphere than was observed. Many papers have been written since then. We discuss the most recent papers on this subject and using the latest data show that the differences remain unresolved.

Po-Chedley and Fu investigated the difference in the magnitude of global temperature trends generated from the Microwave Sounding Unit (MSU) for the midtroposphere (TMT, surface to about 75 hPa) between the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). Their approach was to examine the magnitude of a noise-reduction coefficient of one short-lived satellite, NOAA-9, which differed from UAH and RSS. Using radiosonde comparisons over a 2-yr period, they calculated an adjustment to the UAH coefficient that, when applied to the UAH data, increased the UAH global TMT trend for 1979–2009 by +0.042 K decade−1, which then happens to agree with RSS’s TMT trend. In studying their analysis, the authors demonstrate 1) the adjustment calculated using radiosondes is inconclusive when errors are accounted for; 2) the adjustment was applied in a manner inconsistent with the UAH satellite merging strategy, creating a larger change than would be generated had the actual UAH methodology been followed; and 3) that trends of a similar product that uses the same UAH coefficient are essentially identical to UAH and RSS. Based on the authors’ previous analysis and additional work here, UAH will continue using the NOAA-9 noise-reduction coefficient, as is, for version 5.4 and the follow-on version 5.5.

Coats raises issues regarding the utility of the snowfall metric presented by Christy in “Searching for information in 133 years of California snowfall observations,” suggesting that variance issues need more attention and that alternative metrics would be more useful than snowfall. Although discussed by Christy, the variance question is further addressed here. Regarding other metrics, it is shown that they are either inconsistently measured for long-term analysis or are actually consistent with Christy’s findings. In addition, it is demonstrated that Tahoe City, discussed by Coats, is inappropriate for examining long-term precipitation trends because of inconsistent measuring practices through time. Christy’s results remain unchanged.

Monthly snowfall totals from over 500 stations in California, some of which date back to 1878, are examined. Most data were accessed through the NOAA archive, but several thousand station months of data were separately keyed in from image files of original documents. Over 26 000 of these entries were new relative to the NOAA archive, generally providing data prior to 1920. The stations were then subdivided into 18 regions for the construction of representative time series of each area. There were problems with the basic data—the most difficult with which to deal was the increasing presence of “zero” totals that should have been recorded as “missing.” This and other issues reduce the confidence that the regional time series are representative of true variations and trends, especially for regions with few systematically reporting stations. Interpreting linear trends on time series with infrequent large anomalies of one sign (i.e., heavy snowfall years) and unresolved data issues should be done with caution. For those regions characterized by consistent monitoring and with the most robust statistical reproducibility, no statistically significant trends in their periods of record (up to 133 years) nor in the most recent 50 years are found. This result encompasses the main snowfall region of the western slope of the Sierra Nevada Mountains.

The severity of drought has many implications for society. Its impacts on rain-fed agriculture are especially direct, however. The southeastern United States, with substantial rain-fed agriculture and large variability in growing-season precipitation, is especially vulnerable to drought. As commodity markets, drought assistance programs, and crop insurance have matured, more advanced information is needed on the evolution and impacts of drought. So far many new drought products and indices have been developed. These products generally do not include spatial details needed in the Southeast or do not include the physiological state of the crop, however. Here, a new type of drought measure is described that incorporates high-resolution physical inputs into a crop model (corn) that evolves based on the physical–biophysical conditions. The inputs include relatively high resolution (as compared with standard surface or NOAA Cooperative Observer Program data) (5 km) radar-derived precipitation, satellite-derived insolation, and temperature analyses. The system (referred to as CropRT for gridded crop real time) is run in real time under script control to provide daily maps of crop evolution and stress. Examples of the results from the system are provided for the 2008–10 growing seasons. Plots of daily crop water stress show small subcounty-scale variations in stress and the rapid change in stress over time. Depictions of final crop yield in comparison with seasonal average stress are provided.

The IPCC AR4 (2007) discussed bulk tropospheric temperatures as an indicator of atmospheric energy content. Here, we examine the latest publications about, and versions of, the AR4 data sets. The metric studied is the trend that represents the average rate of atmospheric energy accumulation that relates to increased greenhouse gas forcing. For temperatures from microwave instruments, UAHuntsville's indicates the lowest trend for 1979-2009 and National Oceanic and Atmospheric Administration-Center for Satellite Applications and Research (NOAA-STAR)'s the highest, being slightly higher than Remote Sensing Systems' (RSS). Updated analyses using radiosonde data suggest RSS and STAR experienced spurious warming after the mid-1990s. When satellite and radiosonde data sets are considered, the global trends for 1979-2009 of the lower and mid-troposphere are +0.15 and +0.06°C decade-1 respectively. Error ranges of these estimates, if we do not apply information that indicates some data sets contain noticeable trend problems, are at least ±0.05°C decade-1, which needs reduction to characterize forcing and response in the climate system accurately.

A time series (1916–2009) of annual snowfall totals for Huntington Lake (HL, elev. 2141 m) in the southern Sierra Nevada of California is reconstructed. A reconstruction is (a) necessary because HL data after 1972 are mostly missing and (b) possible because nearby stations reveal high correlations with HL, two above 0.90. The results show mean annual snowfall in HL is 624 cm with an insignificant trend of +0.5 cm (+0.08%) ±13.1 cm decade−1. Similar positive but insignificant trends for spring snowfall were also calculated. Annual stream flow and precipitation trends for the region again were insignificantly positive for the same period. Snow-water-equivalent comparisons, measured on 1 Apr since 1930 at 26 sites and since 1950 at 45, show similar small, mostly positive, and insignificant trends. These results combined with published temperature time series, which also reveal no significant trends, form a consistent picture of no remarkable long-term changes in the snowfall of this area and elevation of the southern Sierra Nevada of California since the early 20th century.

A Japanese long-term reanalysis (JRA-25) was completed in 2006 utilizing the comprehensive set of observations from the 40-yr ECMWF Re-Analysis (ERA-40). JRA-25 and ERA-40 adopted the same type of assimilation systems: 3DVAR with direct use of satellite sounding radiances. Long-term upper-air thermal tendencies in both reanalyses are examined and compared with the observational deep-layer temperatures of the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). The upper-air temperature tendencies in the reanalyses are significantly different from those of UAH and RSS, and they appear to be influenced by the way the observations of the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) are used. This study focuses on documenting problems in TOVS assimilation, especially problems in bias corrections used in the reanalyses. Referring to quantitative results in an examination of biases between the reanalyses and raw TOVS observations, this study identifies (i) spurious thermal tendencies derived from transitions in TOVS and in the reanalysis calculation streams, (ii) an excessive enhancement of the tropical water cycle in ERA-40, and (iii) an excessive cooling trend and unstable behavior in the stratospheric temperature in JRA-25. The results of this study suggest that any inconsistencies in TOVS usage can lead to serious inconsistencies in the reanalyses. Therefore, time-consuming efforts to obtain reliable observational information from TOVS are necessary for further progress in reanalyses.

Surface temperatures have been observed in East Africa for more than 100 yr, but heretofore have not been subject to a rigorous climate analysis. To pursue this goal monthly averages of maximum (TMax), minimum (TMin), and mean (TMean) temperatures were obtained for Kenya and Tanzania from several sources. After the data were organized into time series for specific sites (60 in Kenya and 58 in Tanzania), the series were adjusted for break points and merged into individual gridcell squares of 1.25°, 2.5°, and 5.0°. Results for the most data-rich 5° cell, which includes Nairobi, Mount Kilimanjaro, and Mount Kenya, indicate that since 1905, and even recently, the trend of TMax is not significantly different from zero. However, TMin results suggest an accelerating temperature rise. Uncertainty estimates indicate that the trend of the difference time series (TMax − TMin) is significantly less than zero for 1946–2004, the period with the highest density of observations. This trend difference continues in the most recent period (1979–2004), in contrast with findings in recent periods for global datasets, which generally have sparse coverage of East Africa. The differences between TMax and TMin trends, especially recently, may reflect a response to complex changes in the boundary layer dynamics; TMax represents the significantly greater daytime vertical connection to the deep atmosphere, whereas TMin often represents only a shallow layer whose temperature is more dependent on the turbulent state than on the temperature aloft. Because the turbulent state in the stable boundary layer is highly dependent on local land use and perhaps locally produced aerosols, the significant human development of the surface may be responsible for the rising TMin while having little impact on TMax in East Africa. This indicates that time series of TMax and TMin should become separate variables in the study of long-term changes.

The temperature records of 28 Australian radiosonde stations were compared with the bulk-layer temperatures of three satellite products of The University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS) for the period 1979–2006. The purpose was to use the satellite data as “reference truth” to quantify the effect of changes in station equipment, software, and operations on the reported upper air temperatures and resulting trends. The products are lower troposphere (LT), midtroposphere (MT), and lower stratosphere (LS). Four periods of significant shifts in temperatures were found in the radiosondes relative to both satellite datasets. In the first two shifts—around 1982/83 and 1987/88—the radiosondes experienced an accumulated LT and MT warming shift of 0.5 K on average. These shifts coincided with equipment changes. If unadjusted for these shifts, the radiosondes report spurious tropospheric warming of almost 0.2 K decade−1. For LS in the first period, there is relative warming but in the second, cooling. If unadjusted, the radiosondes overstate LS cooling by about −0.15 K decade−1. The third (early 1990s) and fourth (1998 LT and MT and 2002 LS) shifts are less robustly connected to changes in the radiosondes. Errors in the construction methodology of the satellite products likely account for at least part of the discrepancies but cannot be attributed with confidence to a specific cause. Having opposite signs in the two periods, the last two discrepancies tend to cancel each other. The net effect of these last two shifts on the overall LT and MT trends of ±0.03 K decade−1 is small.

We examine tropospheric temperature trends of 67 runs from 22 ‘Climate of the 20th Century’ model simulations and try to reconcile them with the best available updated observations (in the tropics during the satellite era). Model results and observed temperature trends are in disagreement in most of the tropical troposphere, being separated by more than twice the uncertainty of the model mean. In layers near 5 km, the modelled trend is 100 to 300% higher than observed, and, above 8 km, modelled and observed trends have opposite signs. These conclusions contrast strongly with those of recent publications based on essentially the same data. Copyright © 2007 Royal Meteorological Society

Radiosonde datasets of temperature often suffer from discontinuities due to changes in instrumentation, location, observing practices, and algorithms. To identify temporal discontinuities that affect the VIZ/Sippican family of radiosondes, the 1979–2004 time series of a composite of 31 VIZ stations are compared to composites of collocated values of layer temperatures from two microwave sounding unit datasets—the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). Discontinuities in the radiosonde time series relative to the two satellite datasets were detected with high significance and with similar magnitudes; however, some instances occurred where only one satellite dataset differed from the radiosondes. For the products known as lower troposphere (LT; surface–300 hPa) and midtroposphere (MT; surface–75-hPa layer), significant discontinuities relative to both satellite datasets were found—two cases for LT and four for MT. These are likely associated with changes in the radiosonde system. Three apparent radiosonde discontinuities were also determined for the lower-stratospheric product (LS; 150–15 hPa). Because they cannot be definitely traced to changes in the radiosonde system, they could be the result of common errors in the satellite products. When adjustments are applied to the radiosondes based independently on each satellite dataset, 26-yr trends of UAH (RSS) are consistent with the radiosondes for LT, MT, and LS at the level of ±0.06, ±0.04, and ±0.07 (±0.12, ±0.10, and ±0.10) K decade−1. Also, simple statistical retrievals based on radiosonde-derived relationships of LT, MT, and LS indicate a higher level of consistency with UAH products than with those of RSS.

The problems inherent in the estimation of global tropospheric temperature trends from a combination of near-nadir Microwave Sounding Unit (MSU) channel-2 and -4 data are described. The authors show that insufficient overlap between those two channels’ weighting functions prevents a physical removal of the stratospheric influence on tropospheric channel 2 from the stratospheric channel 4. Instead, correlations between stratospheric and tropospheric temperature fluctuations based upon ancillary (e.g., radiosonde) information can be used to statistically estimate a correction for the stratospheric influence on MSU 2 from MSU 4. Fu et al. developed such a regression relationship from radiosonde data using the 850–300-hPa layer as the target predictand. There are large errors in the resulting fit of the two MSU channels to the tropospheric target layer, so the correlations from the ancillary data must be relied upon to provide a statistical minimization of the resulting errors. Such relationships depend upon the accuracy of the particular training dataset as well as the dataset time period and its global representativeness (i.e., temporal and spatial stationarity of the statistics). It is concluded that near-nadir MSU channels 2 and 4 cannot be combined to provide a tropospheric temperature measure without substantial uncertainty resulting from a necessary dependence on ancillary information regarding the vertical profile of temperature variations, which are, in general, not well known on a global basis.

A procedure is described to construct time series of regional surface temperatures and is then applied to interior central California stations to test the hypothesis that century-scale trend differences between irrigated and nonirrigated regions may be identified. The procedure requires documentation of every point in time at which a discontinuity in a station record may have occurred through (a) the examination of metadata forms (e.g., station moves) and (b) simple statistical tests. From this “homogeneous segments” of temperature records for each station are defined. Biases are determined for each segment relative to all others through a method employing mathematical graph theory. The debiased segments are then merged, forming a complete regional time series. Time series of daily maximum and minimum temperatures for stations in the irrigated San Joaquin Valley (Valley) and nearby nonirrigated Sierra Nevada (Sierra) were generated for 1910–2003. Results show that twentieth-century Valley minimum temperatures are warming at a highly significant rate in all seasons, being greatest in summer and fall (> +0.25°C decade−1). The Valley trend of annual mean temperatures is +0.07° ± 0.07°C decade−1. Sierra summer and fall minimum temperatures appear to be cooling, but at a less significant rate, while the trend of annual mean Sierra temperatures is an unremarkable −0.02° ± 0.10°C decade−1. A working hypothesis is that the relative positive trends in Valley minus Sierra minima (>0.4°C decade−1 for summer and fall) are related to the altered surface environment brought about by the growth of irrigated agriculture, essentially changing a high-albedo desert into a darker, moister, vegetated plain.

There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976‐77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset. The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data.

Deep-layer temperatures derived from satellite-borne microwave sensors since 1979 are revised (version 5.0) to account for 1) a change from microwave sounding units (MSUs) to the advanced MSUs (AMSUs) and 2) an improved diurnal drift adjustment for tropospheric products. AMSU data, beginning in 1998, show characteristics indistinguishable from the earlier MSU products. MSU–AMSU error estimates are calculated through comparisons with radiosonde-simulated bulk temperatures for the low–middle troposphere (TLT), midtroposphere (TMT), and lower stratosphere (TLS.) Monthly (annual) standard errors for global mean anomalies of TLT satellite temperatures are estimated at 0.10°C (0.07°C). The TLT (TMT) trend for January 1979 to April 2002 is estimated as +0.06° (+0.02°) ±0.05°C decade–1 (95% confidence interval). Error estimates for TLS temperatures are less well characterized due to significant heterogeneities in the radiosonde data at high altitudes, though evidence is presented to suggest that since 19...

Lower tropospheric temperature anomalies from the global satellite MSU that have been available since 1979 are unique and play a significant role in the continuing climate debate. A number of investigators have analyzed the MSU data using regression analysis to remove the geo- physical effects of volcanoes, El Nino/Southern Oscillation, and solar irradiance in an effort to deter- mine any underlying trend line. In a recent paper Santer et al. (2001; J Geophys Res 106:28033- 28059) questioned the validity of such studies, noting that large El Nino events have occurred at the same time as 2 major volcanoes. They calculated a correlation between these 2 variables and claimed that this indicates collinearity, which can adversely affect any regression analyses. We examine the issue of collinearity between the volcano and El Nino/Southern Oscillation signals in the analysis of the MSU data. We do this by using the general tests for collinearity of Belsley. There are 2 tests. The first is for degrading collinearity on the data matrix of the predictor variables. If the first test fails, a second test for harmful collinearity is performed on the coefficients from any regression analysis. Employing these 2 tests, we find that there is no degrading or harmful collinearity used in the mod- eling of the MSU temperature anomalies.

To answer this very common though surprisingly difficult question, a technique was developed to reconstruct a local temperature time series of summer average maximum temperatures in northcentral Alabama since 1893. The results show that the warmest summer was 1925 at 34.9° ±0.4°C but that 5 other years are statistically so close they could not be eliminated as contenders. (The trend is -0.13°C decade-1.) Our insistence that this ambiguity be recognizedby the inquirer, usually the media, causes confusion and reduces their interest level because they desire anabsolute answer to, in their view, a very simple question.

In 2013, the vast majority of the monitored climate variables reported here maintained trends established in recent decades. ENSO was in a neutral state during the entire year, remaining mostly on the cool side of neutral with modest impacts on regional weather patterns around the world. This follows several years dominated by the effects of either La Niña or El Niño events. According to several independent analyses, 2013 was again among the 10 warmest years on record at the global scale, both at the Earths surface and through the troposphere. Some regions in the Southern Hemisphere had record or near-record high temperatures for the year. Australia observed its hottest year on record, while Argentina and New Zealand reported their second and third hottest years, respectively. In Antarctica, Amundsen-Scott South Pole Station reported its highest annual temperature since records began in 1957. At the opposite pole, the Arctic observed its seventh warmest year since records began in the early 20th century. At 20-m depth, record high temperatures were measured at some permafrost stations on the North Slope of Alaska and in the Brooks Range. In the Northern Hemisphere extratropics, anomalous meridional atmospheric circulation occurred throughout much of the year, leading to marked regional extremes of both temperature and precipitation. Cold temperature anomalies during winter across Eurasia were followed by warm spring temperature anomalies, which were linked to a new record low Eurasian snow cover extent in May. Minimum sea ice extent in the Arctic was the sixth lowest since satellite observations began in 1979. Including 2013, all seven lowest extents on record have occurred in the past seven years. Antarctica, on the other hand, had above-average sea ice extent throughout 2013, with 116 days of new daily high extent records, including a new daily maximum sea ice area of 19.57 million km2 reached on 1 October. ENSO-neutral conditions in the eastern central Pacific Ocean and a negative Pacific decadal oscillation pattern in the North Pacific had the largest impacts on the global sea surface temperature in 2013. The North Pacific reached a historic high temperature in 2013 and on balance the globally-averaged sea surface temperature was among the 10 highest on record. Overall, the salt content in nearsurface ocean waters increased while in intermediate waters it decreased. Global mean sea level continued to rise during 2013, on pace with a trend of 3.2 mm yr-1 over the past two decades. A portion of this trend (0.5 mm yr-1) has been attributed to natural variability associated with the Pacific decadal oscillation as well as to ongoing contributions from the melting of glaciers and ice sheets and ocean warming. Global tropical cyclone frequency during 2013 was slightly above average with a total of 94 storms, although the North Atlantic Basin had its quietest hurricane season since 1994. In the Western North Pacific Basin, Super Typhoon Haiyan, the deadliest tropical cyclone of 2013, had 1-minute sustained winds estimated to be 170 kt (87.5 m s-1) on 7 November, the highest wind speed ever assigned to a tropical cyclone. High storm surge was also associated with Haiyan as it made landfall over the central Philippines, an area where sea level is currently at historic highs, increasing by 200 mm since 1970. In the atmosphere, carbon dioxide, methane, and nitrous oxide all continued to increase in 2013. As in previous years, each of these major greenhouse gases once again reached historic high concentrations. In the Arctic, carbon dioxide and methane increased at the same rate as the global increase. These increases are likely due to export from lower latitudes rather than a consequence of increases in Arctic sources, such as thawing permafrost. At Mauna Loa, Hawaii, for the first time since measurements began in 1958, the daily average mixing ratio of carbon dioxide exceeded 400 ppm on 9 May. The state of these variables, along with dozens of others, and the 2013 climate conditions of regions around the world are discussed in further detail in this 24th edition of the State of the Climate series. © 2014, American Meteorological Society. All rights reserved.

Described herein is the first version release of monthly temperature holdings of a new Global Land Surface Meteorological Databank. Organized under the auspices of the International Surface Temperature Initiative (ISTI), an international group of scientists have spent three years collating and merging data from numerous sources to create a merged holding. This release in its recommended form consists of over 30 000 individual station records, some of which extend over the past 300 years. This article describes the sources, the chosen merge methodology, and the resulting databank characteristics. Several variants of the databank have also been released that reflect the structural uncertainty in merging datasets. Variants differ in, for example, the order in which sources are considered and the degree of congruence required in station geolocation for consideration as a merged or unique record. Also described is a version control protocol that will be applied in the event of updates. Future updates are envisaged with the addition of new data sources, and with changes in processing, where public feedback is always welcomed. Major updates, when necessary, will always be accompanied by a new journal paper. This databank release forms the foundation for the construction of new global land surface air temperature analyses by the global research community and their assessment by the ISTI's benchmarking and assessment working group.

A variety of measurements indicate that the rate of atmospheric warming in the tropics since 1979 is less than the observed warming of the sea surface. This result is further examined using the high quality buoys monitored by the Pacific Marine Environmental Laboratory in the Tropical Pacific Ocean. These buoys show cooling (most cases being statistically significant) of the air at 3m height relative to the sea at l m depth over 8 to 20-year periods in the eastern region. A global surface temperature dataset which uses only near-surface air temperature over both land and ocean, indicates less warming since 1979 than those using SSTs over the oceans, though large uncertainties remain with marine air temperatures.

We determined the seasonal environmental conditions during the Little Ice Age (LIA) by interpreting isotope proxies in the coral skeleton of Montastrea faveolata from the northeast Caribbean. The oxygen isotope composition was determined for three time intervals during the LIA (1700–1705, 1780–1785, 1810–1815), thought to correspond to the coldest intervals. The period 1984–1989 was used to represent modern calibration conditions. We determined that SSTs from the LIA intervals are nearly 2–3°C cooler than present. LIA cooling in the Caribbean may have resulted from regional oceanic and atmospheric circulation differences, especially in winter. We propose that a trough of cold air from the north Atlantic may have extended farther south than present, into the northern Caribbean. Although there are indications from Pacific corals that seasonality was greater during the LIA, this does not seem to be the case for the Caribbean.

Two deep-layer tropospheric temperature products, one for the lower troposphere (T2LT) and one for the midtroposphere (T2, which includes some stratospheric emissions), are based on the observations of channel 2 of the microwave sounding unit on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Revisions to version C of these datasets have been explicitly applied to account for the effects of orbit decay (loss of satellite altitude) and orbit drift (east–west movement). Orbit decay introduces an artificial cooling in T2LT, while the effects of orbit drift introduce artificial warming in both T2LT and T2. The key issues for orbit drift are 1) accounting for the diurnal cycle and 2) the adjustment needed to correct for spurious effects related to the temperature of the instrument. In addition, new calibration coefficients for NOAA-12 have been applied. The net global effect of these revisions (version D) is small, having little impact on the year-to-year anomalies. T...

A comprehensive comparison is made between two tropospheric temperature datasets over the period 1979–98: the most recent and substantially revised (version d) microwave sounding unit (MSU) channel 2 data retrievals, and a gridded radiosonde analysis provided by the Hadley Centre of the U.K. Meteorological Office. The latter is vertically weighted to approximate the deep layer temperatures measured by the satellite data. At individual grid points, there is good overall agreement among monthly anomalies, especially over the Northern Hemisphere continents where the climate signal is large, although monthly root-mean-square (rms) differences typically exceed 0.6°C. Over the Tropics, correlations are lower and rms differences can be as large as the standard deviations of monthly anomalies. Differences in the gridpoint variances are significant at many locations, which presumably reflects sources of noise in one or both measurement systems. It is often argued for climate purposes that temperature anomalies are...

We examine to what degree we can expect to obtain accurate temperature trends for the last two decades near the surface and in the lower troposphere. We compare temperatures obtained from surface observations and radiosondes as well as satellite-based measurements from the Microwave Soundings Units (MSU), which have been adjusted for orbital decay and non-linear instrument-body effects, and reanalyses from the European Centre for Medium-Range Weather Forecasts (ERA) and the National Centre for Environmental Prediction (NCEP). In regions with abundant conventional data coverage, where the MSU has no major influence on the reanalysis, temperature anomalies obtained from microwave sounders, radiosondes and from both reanalyses agree reasonably. Where coverage is insufficient, in particular over the tropical oceans, large differences are found between the MSU and either reanalysis. These differences apparently relate to changes in the satellite data availability and to differing satellite retrieval methodologies, to which both reanalyses are quite sensitive over the oceans. For NCEP, this results from the use of raw radiances directly incorporated into the analysis, which make the reanalysis sensitive to changes in the underlying algorithms, e.g. those introduced in August 1992. For ERA, the bias-correction of the one-dimensional variational analysis may introduce an error when the satellite relative to which the correction is calculated is biased itself or when radiances change on a time scale longer than a couple of months, e.g. due to orbit decay. ERA inhomogeneities are apparent in April 1985, October/November 1986 and April 1989. These dates can be identified with the replacements of satellites. It is possible that a negative bias in the sea surface temperatures (SSTs) used in the reanalyses may have been introduced over the period of the satellite record. This could have resulted from a decrease in the number of ship measurements, a concomitant increase in the importance of satellite-derived SSTs, and a likely cold bias in the latter. Alternately, a warm bias in SSTs could have been caused by an increase in the percentage of buoy measurements (relative to deeper ship intake measurements) in the tropical Pacific. No indications for uncorrected inhomogeneities of land surface temperatures could be found. Near-surface temperatures have biases in the boundary layer in both reanalyses, presumably due to the incorrect treatment of snow cover. The increase of near-surface compared to lower tropospheric temperatures in the last two decades may be due to a combination of several factors, including high-latitude near-surface winter warming due to an enhanced NAO and upper-tropospheric cooling due to stratospheric ozone decrease.