Peter W. Thorne

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and Intercomparisons

AbstractThe monthly Extended Reconstructed Sea Surface Temperature (ERSST) dataset, available on global 2° × 2° grids, has been revised herein to version 4 (v4) from v3b. Major revisions include updated and substantially more complete input data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) release 2.5; revised empirical orthogonal teleconnections (EOTs) and EOT acceptance criterion; updated sea surface temperature (SST) quality control procedures; revised SST anomaly (SSTA) evaluation methods; updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2); and buoy SST bias adjustment not previously made in v3b.Tests show that the impacts of the revisions to ship SST bias adjustment in ERSST.v4 are dominant among all revisions and updates. The effect is to make SST 0.1°–0.2°C cooler north of 30°S but 0.1°–0.2°C warmer south of 30°S in ERSST.v4 than in ERSST.v3b before 1940. In comparison with the Met Office SST product...

Attribution of observed surface humidity changes to human influence

Water vapour is the most important contributor to the natural greenhouse effect, and the amount of water vapour in the atmosphere is expected to increase under conditions of greenhouse-gas-induced warming, leading to a significant feedback on anthropogenic climate change. Theoretical and modelling studies predict that relative humidity will remain approximately constant at the global scale as the climate warms, leading to an increase in specific humidity. Although significant increases in surface specific humidity have been identified in several regions, and on the global scale in non-homogenized data, it has not been shown whether these changes are due to natural or human influences on climate. Here we use a new quality-controlled and homogenized gridded observational data set of surface humidity, with output from a coupled climate model, to identify and explore the causes of changes in surface specific humidity over the late twentieth century. We identify a significant global-scale increase in surface specific humidity that is attributable mainly to human influence. Specific humidity is found to have increased in response to rising temperatures, with relative humidity remaining approximately constant. These changes may have important implications, because atmospheric humidity is a key variable in determining the geographical distribution and maximum intensity of precipitation, the potential maximum intensity of tropical cyclones, and human heat stress, and has important effects on the biosphere and surface hydrology.

Revisiting radiosonde upper air temperatures from 1958 to 2002

HadAT is a new analysis of the global upper air temperature record from 1958 to 2002 based upon radiosonde data alone. This analysis makes use of a greater number of stations than previous radiosonde analyses, combining a number of digital data sources. Neighbor buddy checks are applied to ensure that both spatial and temporal consistency are maintained. A framework of previously quality controlled stations is used to define the initial station network to minimize the effects of any pervasive biases in the raw data upon the adjustments. The analysis is subsequently expanded to consider all remaining available long-term records. The final data set consists of 676 radiosonde stations, with a bias toward continental Northern Hemisphere midlatitudes. Temperature anomaly time series are provided on 9 mandatory reporting pressure levels from 850 to 30 hPa. The effects of sampling and adjustment uncertainty are calculated at all scales from the station series to the global mean and from seasonal to multidecadal. These estimates are solely parametric uncertainty, given our methodological choices, and not structural uncertainty which relates to sensitivity to choice of approach. An initial analysis of HadAT does not fundamentally alter our understanding of long-term changes in upper air temperature changes.

Recent Changes in Surface Humidity: Development of the HadCRUH Dataset

Water vapor constitutes the most significant greenhouse gas, is a key driver of many atmospheric processes, and hence, is fundamental to understanding the climate system. It is a major factor in human “heat stress,” whereby increasing humidity reduces the ability to stay cool. Until now no truly global homogenized surface humidity dataset has existed with which to assess recent changes. The Met Office Hadley Centre and Climatic Research Unit Global Surface Humidity dataset (HadCRUH), described herein, provides a homogenized quality controlled near-global 5° by 5° gridded monthly mean anomaly dataset in surface specific and relative humidity from 1973 to 2003. It consists of land and marine data, and is geographically quasicomplete over the region 60°N–40°S. Between 1973 and 2003 surface specific humidity has increased significantly over the globe, tropics, and Northern Hemisphere. Global trends are 0.11 and 0.07 g kg 1 (10 yr) 1 for land and marine components, respectively. Trends are consistently larger in the tropics and in the Northern Hemisphere during summer, as expected: warmer regions exhibit larger increases in specific humidity for a given temperature change under conditions of constant relative humidity, based on the Clausius–Clapeyron equation. Relative humidity trends are not significant when averaged over the landmass of the globe, tropics, and Northern Hemisphere, although some seasonal changes are significant. A strong positive bias is apparent in marine humidity data prior to 1982, likely owing to a known change in reporting practice for dewpoint temperature at this time. Consequently, trends in both specific and relative humidity are likely underestimated over the oceans.

UNCERTAINTIES IN CLIMATE TRENDS Lessons from Upper-Air Temperature Records

Historically, meteorological observations have been made for operational forecasting rather than long-term monitoring purposes, so that there have been numerous changes in instrumentation and procedures. Hence to create climate quality datasets requires the identification, estimation, and removal of many nonclimatic biases from the historical data. Construction of a number of new tropospheric temperature climate datasets has highlighted previously unrecognized uncertainty in multidecadal temperature trends aloft. The choice of dataset can even change the sign of upper-air trends relative to those reported at the surface. So structural uncertainty introduced unintentionally through dataset construction choices is important and needs to be understood and mitigated. A number of ways that this could be addressed for historical records are discussed, as is the question of How it needs to be reduced through future coordinated observing systems with long-term monitoring as a driver, enabling explicit calculation...

U.S. Climate Reference Network after One Decade of Operations: Status and Assessment

The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAAs National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restric...

Separating signal and noise in atmospheric temperature changes: The importance of timescale

We compare global-scale changes in satellite estimates of the temperature of the lower troposphere (TLT) with model simulations of forced and unforced TLT changes. While previous work has focused on a single period of record, we select analysis timescales ranging from 10 to 32 years, and then compare all possible observed TLT trends on each timescale with corresponding multi-model distributions of forced and unforced trends. We use observed estimates of the signal component of TLT changes and model estimates of climate noise to calculate timescale-dependent signal-to-noise ratios (S/N). These ratios are small (less than 1) on the 10-year timescale, increasing to more than 3.9 for 32-year trends. This large change in S/N is primarily due to a decrease in the amplitude of internally generated variability with increasing trend length. Because of the pronounced effect of interannual noise on decadal trends, a multi-model ensemble of anthropogenically-forced simulations displays many 10-year periods with little warming. A single decade of observational TLT data is therefore inadequate for identifying a slowly evolving anthropogenic warming signal. Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature. Copyright 2011 by the American Geophysical Union.

Reanalyses Suitable for Characterizing Long-Term Trends

Reanalyses are, by a substantial margin, the most utilized climate data products, and they are applied in a myriad of different contexts. Despite their popularity, there are substantial concerns about their suitability for the monitoring of long-term climate trends. This has led to calls for a truly “climate quality” reanalysis that retains long-term trend fidelity. The authors contend that for such a reanalysis to be achieved, a substantial rethinking of the current strategy for producing reanalysis products is required. First, the problem must be defined clearly. Second, the methodology that is employed must be reconsidered so as to minimize potential nonclimatic artifacts and robustly ascertain the inevitable residual uncertainty. Finally, a set of validation data and metrics must be constructed that the community can use to compare and unambiguously assess the claims of climate quality. The purpose of this essay is very much to initiate discussions to this end rather than to prescribe solutions.

Transient Climate Simulations with the HadGEM1 Climate Model: Causes of Past Warming and Future Climate Change

The ability of climate models to simulate large-scale temperature changes during the twentieth century when they include both anthropogenic and natural forcings and their inability to account for warming over the last 50 yr when they exclude increasing greenhouse gas concentrations has been used as evidence for an anthropogenic influence on global warming. One criticism of the models used in many of these studies is that they exclude some forcings of potential importance, notably from fossil fuel black carbon, biomass smoke, and land use changes. Herein transient simulations with a new model, the Hadley Centre Global Environmental Model version 1 (HadGEM1), are described, which include these forcings in addition to other anthropogenic and natural forcings, and a fully interactive treatment of atmospheric sulfur and its effects on clouds. These new simulations support previous work by showing that there was a significant anthropogenic influence on near-surface temperature change over the last century. They demonstrate that black carbon and land use changes are relatively unimportant for explaining global mean near-surface temperature changes. The pattern of warming in the troposphere and cooling in the stratosphere that has been observed in radiosonde data since 1958 can only be reproduced when the model includes anthropogenic forcings. However, there are some discrepancies between the model simulations and radiosonde data, which are largest where observational uncertainty is greatest in the Tropics and high latitudes. Predictions of future warming have also been made using the new model. Twenty-first-century warming rates, following policy-relevant emissions scenarios, are slightly greater in HadGEM1 than in the Third Hadley Centre Coupled Ocean–Atmosphere General Circulation Model (HadCM3) as a result of the extra forcing in HadGEM1. An experiment in which greenhouse gases and other anthropogenic forcings are stabilized at 2100 levels and held constant until 2200 predicts a committed twenty-second-century warming of less than 1 K, whose spatial distribution resembles that of warming during the twenty-first century, implying that the local feedbacks that determine the pattern of warming do not change significantly.

Reanalyses suitable for characterizing long-term trends: are they realy achievable?

Reanalyses are, by a substantial margin, the most utilized climate data products, and they are applied in a myriad of different contexts. Despite their popularity, there are substantial concerns about their suitability for the monitoring of long-term climate trends. This has led to calls for a truly “climate quality” reanalysis that retains long-term trend fidelity. The authors contend that for such a reanalysis to be achieved, a substantial rethinking of the current strategy for producing reanalysis products is required. First, the problem must be defined clearly. Second, the methodology that is employed must be reconsidered so as to minimize potential nonclimatic artifacts and robustly ascertain the inevitable residual uncertainty. Finally, a set of validation data and metrics must be constructed that the community can use to compare and unambiguously assess the claims of climate quality. The purpose of this essay is very much to initiate discussions to this end rather than to prescribe solutions.