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A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

The article reports on the climate impacts of anthropogenic carbon dioxide emissions. In its Fourth Assessment Report, the Intergovernmental Panel on Climate Change (IPCC) suggests that about 50 percent of a carbon dioxide increase will be removed from the atmosphere within 30 years. It is reported that the logarithmic relationship between carbon dioxide and its radiative forcing implies that the time scale at which atmospheric temperature declines will be longer.

Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S-80°N, 1979-2016) = 0.174°C decade−1], ~30% larger than our previous version of the dataset [global trend (70°S-80°N, 1979-2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics. [ABSTRACT FROM AUTHOR]

Temperature observations of the upper-air atmosphere are now available for more than 40 years from both ground- and satellite-based observing systems. Recent years have seen substantial improvements in reducing long-standing discrepancies among datasets throughmajor reprocessing efforts. The advent of radio occultation (RO) observations in 2001 has led to further improvements in vertically resolved temperature measurements, enabling a detailed analysis of upper-troposphere/lower-stratosphere trends. This paper presents the current state of atmospheric temperature trends from the latest available observational records. We analyze observations from merged operational satellite measurements, radiosondes, lidars, and RO, spanning a vertical range fromthe lower troposphere to the upper stratosphere. The focus is on assessing climate trends and on identifying the degree of consistency among the observational systems. The results showa robust cooling of the stratosphere of about 1-3 K, and a robust warming of the troposphere of about 0.6-0.8K over the last four decades (1979- 2018). Consistent results are found between the satellite-based layer-average temperatures and vertically resolved radiosonde records. The overall latitude-altitude trend patterns are consistent between RO and radiosonde records. Significant warming of the troposphere is evident in the RO measurements available after 2001, with trends of 0.25-0.35K per decade. Amplified warming in the tropical upper-troposphere compared to surface trends for 2002-18 is found based on ROand radiosonde records, in approximate agreement withmoist adiabatic lapse rate theory. The consistency of trend results from the latest upper-air datasets will help to improve understanding of climate changes and their drivers. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

Decadal prediction is a relatively new branch of climate science that bridges the gap between seasonal climate forecasts and multidecadal-to-century projections of climate change. This paper develops a three-step framework toward the potential application of decadal temperature predictions using the Community Climate System Model, version 4 (CCSM4). In step 1, the predictions are evaluated and it is found that the temperature hindcasts show skill over some regions of the United States and Canada. In step 2, the predictions are manipulated using two methods: a deterministic-anomaly approach (like climate change projections) and a probabilistic tercile-based approach (like seasonal forecasts). In step 3, the predictions are translated by adding a delta (for the anomaly manipulation) and conducting a weighted resample (for the probabilistic manipulation), as well as using a new hybrid method. Using the 2010 initialized hindcast, the framework is demonstrated for predicting 2011-15 over two case-study watersheds [Ottawa (Canada) and Colorado]. For the Colorado watershed, there was a noticeable shift toward higher temperatures, and the delta, weighted resample, and hybrid translations all were better at capturing the observed temperatures than was an approach that used climatological values. For the Ottawa watershed, the observed temperatures over the period of prediction were only subtly different than the climatological values; therefore, the difference between the translation methods was less noticeable. The advantages and disadvantages of the manipulation and translation approaches are discussed, as well as how their use will depend on the user context. The authors emphasize that skill evaluations should be tailored to particular applications and identify additional steps that are needed before the decadal temperature predictions can be readily incorporated into applications. [ABSTRACT FROM AUTHOR]

The aim of the paper is to investigate the question of how a changing climate influences the spreading of pollutants on continental and global scales. For characterizing the spreading, a measure of chaotic systems, called topological entropy, is used. This quantity describes the exponential stretching of pollutant clouds and, therefore, is related to the predictability and the complexity of the structure of a pollutant cloud. For the dispersion simulations the ERA-Interim database is used from 1979 to 2015. The simulations demonstrate that during this period the mean topological entropy slightly increases: the length of an initially line-like pollutant cloud advected for 10 (30) days in the atmosphere becomes 20%-65% (200%-400%) longer by the 2010s than in the 1980s. The mean topological entropy is found to be strongly correlated with the mean of the absolute value of the relative vorticity and only weakly linked to the mean temperature. [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change's (IPCC) 'very likely' statement that anthropogenic emissions are affecting climate is based on a statistical detection and attribution methodology that strongly depends on the characterization of internal climate variability. In this paper, the authors test the robustness of this statement in the case of global mean surface air temperature, under different representations of such variability. The contributions of the different natural and anthropogenic forcings to the global mean surface air temperature response are computed using a box diffusion model. Representations of internal climate variability are explored using simple stochastic models that nevertheless span a representative range of plausible temporal autocorrelation structures, including the short-memory first-order autoregressive [AR(1)] process and the long-memory fractionally differencing process. The authors find that, independently of the representation chosen, the greenhouse gas signal remains statistically significant under the detection model employed in this paper. The results support the robustness of the IPCC detection and attribution statement for global mean temperature change under different characterizations of internal variability, but they also suggest that a wider variety of robustness tests, other than simple comparisons of residual variance, should be performed when dealing with other climate variables and/or different spatial scales. [ABSTRACT FROM AUTHOR]

The paper demonstrates that sudden stratospheric warmings (SSWs) can be simulated in an ensemble of dry dynamical cores that miss the typical SSW forcing mechanisms like moist processes, land-sea contrasts, or topography. These idealized general circulation model (GCM) simulations are driven by a simple Held-Suarez-Williamson (HSW) temperature relaxation and low-level Rayleigh friction. In particular, the four dynamical cores of NCAR's Community Atmosphere Model, version 5 (CAM5), are used, which are the semi-Lagrangian (SLD) and Eulerian (EUL) spectral-transform models and the finite-volume (FV) and the spectral element (SE) models. Three research themes are discussed. First, it is shown that SSW events in such idealized simulations have very realistic flow characteristics that are analyzed via the SLD model. A single vortex-split event is highlighted that is driven by wavenumber-1 and -2 wave-mean flow interactions. Second, the SLD simulations are compared to the EUL, FV, and SE dynamical cores, which sheds light on the impact of the numerical schemes on the circulation. Only SLD produces major SSWs, while others only exhibit minor stratospheric warmings. These differences are caused by SLD's more vigorous wave-mean flow interactions in addition to a warm pole bias, which leads to relatively weak polar jets in SLD. Third, it is shown that tropical quasi-biennial oscillation (QBO)-like oscillations and SSWs can coexist in such idealized HSW simulations. They are present in the SLD dynamical core that is used to analyze the QBO-SSW interactions via a transformed Eulerian-mean (TEM) analysis. The TEM results provide support for the Holton-Tan effect. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years'' (2000--06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality--related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047--53) and present (2000--06) simulation results driven by PCM data and is presented in a separate paper. [ABSTRACT FROM AUTHOR]

A currently popular idea is that El Niño–Southern Oscillation (ENSO) can be viewed as a linear deterministic system forced by noise representing processes with periods shorter than ENSO. Also, there is observational evidence to suggest that the Madden–Julian oscillation (MJO) acts to trigger and/or amplify the warm phase of ENSO in this way. The feedback of the slower process, ENSO, to higher-frequency atmospheric phenomena, of which a large part of the variability in the intraseasonal band is due to the MJO, has received little attention. This paper considers the hypothesis that the probability of an El Niño event is modified by high MJO activity and that, in turn, the MJO is regulated by ENSO activity. If this is indeed the case, then viewing ENSO as a low-frequency oscillation forced by additive stochastic noise would not present a complete picture. This paper tests the above hypothesis using a stochastically forced intermediate coupled model by allowing ENSO to directly influence the stochastic forcing. The model response to a variety of stochastic forcing types is found to be sensitive to the type of forcing applied. When the model is operated beyond its intrinsic Hopf bifurcation, its probability distribution function (PDF) is fundamentally altered when the stochastic forcing is changed from additive to multiplicative. The model integration period also influences the shape of the PDF, which is also compared to the PDF derived from observations. It is found that multiplicative stochastic forcing reproduces some measures of the observations better than the additive stochastic forcing. [ABSTRACT FROM AUTHOR]

As an additional classification to Köppen's climate classification for polar (E) climates, the Polar Marine (EM) climate was presented nearly five decades ago and is revisited in this paper. The EM climate was traced to the North Atlantic, North Pacific, and Southern Ocean and recognized as wet, cloudy, and windy, especially during winter. These areas by definition are encompassed by monthly mean air temperatures of −6.7°C (20°F) and 10°C (50°F) in the coldest and warmest months of the annual cycle, respectively. Here three global reanalyses [ECMWF Interim Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Japan Meteorological Agency (JMA) 25-yr reanalysis (JRA-25)] are used to produce a modern depiction of EM climate. General agreement is found between original and new EM boundaries, for which the poleward boundary can be approximated by the winter sea ice maximum and the equatorward boundary by the warmest month SSTs. Variability of these parameters is shown to largely dictate the EM area. A downward trend in global EM areal extent for 1979-2010 (−42.4 × 109 m2 yr−1) is dominated by the negative Northern Hemisphere (NH) EM trend (−45.7 × 109 m2 yr−1), whereas the Southern Hemisphere (SH) EM areal trend is insignificant. This observed reduction in NH EM areal extent of roughly 20% over the past three decades, largely from losses at the equatorward boundaries of these biologically rich EM zones, may not be fully compensated by poleward shifts in the EM environment due to projected warming and sea ice decline in the twenty-first century. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of different ocean initialization strategies on the forecast skill of decadal prediction experiments performed with the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) coupled model. The ocean initializations assimilate three-dimensional temperature and salinity anomalies from two different ocean state estimates, the ocean reanalysis of the German contribution to Estimating the Circulation and Climate of the Ocean (GECCO) and an ensemble of MPI-OM ocean experiments forced with the NCEP-NCAR atmospheric reanalysis. The results show that North Atlantic and Mediterranean sea surface temperature (SST) variations can be skillfully predicted up to a decade ahead and with greater skill than by both uninitialized simulations and persistence forecasts. The regional distribution of SST predictive skill is similar in both initialization approaches; however, higher skill is found for the NCEP hindcasts than for the GECCO hindcasts when a combination of predictive skill measures is used. Skillful predictions of surface air temperature are obtained over northwestern Europe, northern Africa, and central-eastern Asia. The North Atlantic subpolar gyre region stands out as the region with the highest predictive skill beyond the warming trend, in both SST and upper-ocean heat-content predictions. Here the NCEP hindcasts deliver the best results due to a more accurate initialization of the observed variability. The dominant mechanism for North Atlantic climate predictability is of dynamical origin and can be attributed to the initialization of the Atlantic meridional overturning circulation, thus explaining the reoccurrence of high predictive skill within the second pentad of the hindcasts experiments. The results herein demonstrate that ocean experiments forced with the observed history of the atmospheric state constitute a simple but successful alternative strategy for the initialization of skillful climate predictions over the next decade. [ABSTRACT FROM AUTHOR]

This paper presents derivations of some analytical forms for spatial correlations of evolving random fields governed by a white-noise-driven damped diffusion equation that is the analog of autoregressive order 1 in time and autoregressive order 2 in space. The study considers the two-dimensional plane and the surface of a sphere, both of which have been studied before, but here time is introduced to the problem. Such models have a finite characteristic length (roughly the separation at which the autocorrelation falls to 1/ e) and a relaxation time scale. In particular, the characteristic length of a particular temporal Fourier component of the field increases to a finite value as the frequency of the particular component decreases. Some near-analytical formulas are provided for the results. A potential application is to the correlation structure of surface temperature fields and to the estimation of large area averages, depending on how the original datastream is filtered into a distribution of Fourier frequencies (e.g., moving average, low pass, or narrow band). The form of the governing equation is just that of the simple energy balance climate models, which have a long history in climate studies. The physical motivation provided by the derivation from a climate model provides some heuristic appeal to the approach and suggests extensions of the work to nonuniform cases. [ABSTRACT FROM AUTHOR]

During periods of light surface wind, a warm stable layer forms at the ocean surface with a maximum sea surface temperature (SST) in the early afternoon. The diurnal SST amplitude (DSA) associated with these diurnal warm layers (DWLs) can reach several degrees and impact the tropical climate variability. This paper first presents an approach to building a daily time series of the DSA over the tropics between 1979 and 2002. The DSA is computed over 2.5° of latitude–longitude regions using a simple DWL model forced by hourly-interpolated surface radiative and turbulent fluxes given by the 40-yr ECMWF Re-Analysis (ERA-40). One advantage of this approach is the homogeneity of the results given by the relative homogeneity of ERA-40. The approach is validated at the global scale using empirical DWL models reported in the literature and the Surface Velocity Program (SVP) drifters of the Marine Environmental Data Service (MEDS). For the SVP dataset, a new technique is introduced to derive the diurnal variation of the temperature from raw measurements. This DWL time series is used to analyze the potential role of DWLs in the variability of the tropical climate. The perturbation of the surface fluxes by DWLs can give a cooling of the ocean mixed layer as large as 2.5 K yr-1 in some tropical regions. On a daily basis, this flux perturbation is often above 10 W m-2 and sometimes exceeds 50 W m-2. DWLs can be organized on regions up to a few thousand kilometers and can persist for more than 5 days. It is shown that strong DWLs develop above the equatorial Indian Ocean during the suppressed phase of the intraseasonal oscillation (ISO). DWLs may trigger large-scale convective events and favor the eastward propagation of the ISO convective perturbation during boreal winter. This study also suggests that the simple approach presented here may be used as a DWL parameterization for atmospheric general circulation models. [ABSTRACT FROM AUTHOR]

Sea surface height (SSH) variability is presented over the Black Sea during 1993–2005. The 1/4° × 1/4° resolution daily SSH fields are formed using optimal interpolation of available altimeter data. SSH variability reveals distinct maxima in the eastern and western basins, reflecting variations in the corresponding gyres. A joint examination of SSH and sea surface temperature (SST) indicates strong relationship between the two only in winter, with correlations as high as 0.6 or more. This would reflect a steric change in sea surface height due to thermal expansion averaged over a relatively deep winter mixed layer. Newly developed SSH fields also demonstrate a switch to the positive mode of SSH starting from the end of 1996 lasting ≈4 yr. Such a climatic shift is found to be strongly related to large-scale teleconnection patterns. Finally, the daily SSH and SST anomaly fields presented in this paper can supplement various applications in the Black Sea, such as examination of biological production and mesoscale eddy dynamics. [ABSTRACT FROM AUTHOR]

Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade-1) in the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes. [ABSTRACT FROM AUTHOR]

The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis. Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996–2005 decade relative to the 1940–49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources. The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques. [ABSTRACT FROM AUTHOR]

This paper presents an update on the detection and attribution of global annual mean surface air temperature changes, using recently developed climate models. In particular, it applies a new methodology that permits the inclusion of many more general circulation models (GCMs) into the analysis, and it also includes more recent observations. This methodology involves fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. Despite considerable spread in estimated EBM parameters, characteristics of model performance, such as the transient climate response, appear to be more constrained for each of the forcings. The resulting estimated response patterns are provided as input to the standard fingerprinting method used in previous studies. The estimated GCM responses to changes in greenhouse gases are detected in the observed record for all of the GCMs, and are generally found to be consistent with the observed changes; the same is generally true for the responses to changes in stratospheric aerosols from volcanic eruptions. GCM responses to changes in tropospheric sulfate aerosols and solar irradiance also appear consistent with the observed record, although the uncertainty is larger. Greenhouse gas and solar irradiance changes are found to have contributed to a best guess of ∼0.8 and ∼0.3 K warming over the 1901–2005 period, respectively, while sulfate aerosols have contributed a ∼0.4 K cooling. This analysis provides an observationally constrained estimate of future warming, which is found to be fairly robust across GCMs. By 2100, a warming of between about 1.5 and 4.5 K can be expected according to the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B emissions scenario. These results indicate an emerging constraint for global mean surface temperature responses to external forcings across GCMs, which is corroborated in the observed record. This implies that observationally constrained estimates of past warming and predictions of future warming are indeed becoming robust. [ABSTRACT FROM AUTHOR]

This paper examines variations of the surface air temperature (SAT) over China and the globe in the twentieth century simulated by 19 coupled climate models driven by historical natural and anthropogenic forcings. Most models perform well in simulating both the global and the Northern Hemispheric mean SAT evolutions of the twentieth century. The inclusion of natural forcings improves the simulation, in particular for the first half of the century. The reproducibility of the SAT averaged over China is lower than that of the global and hemispheric averages, but it is still acceptable. The contribution of natural forcings to the SAT over China in the first half of the century is not as robust as that to the global and hemispheric averages. No model could successfully produce the reconstructed warming over China in the 1920s. The prescribed natural and anthropogenic forcings in the coupled climate models mainly produce the warming trends and the decadal- to interdecadal-scale SAT variations with poor performances at shorter time scales. The prominent warming trend in the last half of the century over China and its acceleration in recent decades are weakly simulated. There are discrepancies between the simulated and observed regional features of the SAT trend over China. Few models could produce the summertime cooling over the middle part of eastern China (27°–36°N), while two models acceptably produce the meridional gradients of the wintertime warming trends, with north China experiencing larger warming. Limitations of the current state-of-the-art coupled climate models in simulating spatial patterns of the twentieth-century SAT over China cast a shadow upon their capability toward projecting credible geographical distributions of future climate change through Intergovernmental Panel on Climate Change (IPCC) scenario simulations. [ABSTRACT FROM AUTHOR]

Tropical warm pools appear as the primary mode in the distribution of tropical sea surface temperature (SST). Most previous studies have focused on the role of atmospheric processes in homogenizing temperatures in the warm pool and establishing the observed statistical SST distribution. In this paper, a hierarchy of models is used to illustrate both oceanic and atmospheric mechanisms that contribute to the establishment of tropical warm pools. It is found that individual atmospheric processes have competing effects on the SST distribution: atmospheric heat transport tends to homogenize SST, while the spatial structure of atmospheric humidity and surface wind speeds tends to remove homogeneity. The latter effects dominate, and under atmosphere-only processes there is no warm pool. Ocean dynamics counter this effect by homogenizing SST, and it is argued that ocean dynamics is fundamental to the existence of the warm pool. Under easterly wind stress, the thermocline is deep in the west and shallow in the east. Because of this, poleward Ekman transport of water at the surface, compensated by equatorward geostrophic flow below and linked by equatorial upwelling, creates a cold tongue in the east but homogenizes SST in the west, creating a warm pool. High clouds may also homogenize the SST by reducing the surface solar radiation over the warmest water, but the strength of this feedback is quite uncertain. Implications for the role of these processes in climate change are discussed. [ABSTRACT FROM AUTHOR]

The relative importance of the processes responsible for the annual cycle in the upper-ocean heat content in the Peru Current, in the southeastern tropical Pacific, was diagnosed from an oceanic analysis dataset. It was found that the annual cycle of heat content is forced mainly by insolation. However, the ocean dynamical processes play an important role in producing different regional budget characteristics. In a band 500 km from the coast of Peru, the annual heat content changes in this region are relatively large and can be approximated as sea surface temperature (SST) changes in a fixed-depth mixed layer. The annual cycle of the albedo associated with low-level clouds enhances the annual cycle in insolation, which explains the relatively strong annual cycle of heat content. These clouds, to a large extent, act as a feedback to SST, but a small additional forcing, which is proposed to be cold air advection in this paper, is needed to explain the fact that the maximum cloudiness leads the lowest SST by around a month. Ocean dynamics is important closer to the coast, where upwelling acts partly as damping of the heat content changes and forces it to peak earlier than farther offshore. In a band farther to the southwest, locally wind-forced thermocline motions, which become shallower (deeper) in the warm (cool) season, partially cancel the effect of net surface heat fluxes, whose annual cycle is comparable to that in the region previously mentioned, producing a relatively small annual cycle of heat content. The local forcing appears to be associated with the annual meridional displacements of the South Pacific anticyclone. The annual cycle in SST is also relatively small, which is probably due to the changes in the temperature of the water entrained into the mixed layer associated with the thermocline motions, but also to a mixed layer deeper than that closer to the coast. [ABSTRACT FROM AUTHOR]

ABSTRACT In light of numerous studies documenting a decline in the diurnal temperature range (DTR) over much of the globe, some authors have in recent years examined the annual march of the DTR in an effort to understand better the factors that influence the DTR's seasonal variations. These papers show that, over the southern two-thirds of the eastern United States, the DTR's climatology features peaks in spring and autumn and minima in winter and mid-to-late summer. However, the factors responsible for these characteristics remain uncertain. In this study, the DTR climatology of the eastern United States is analyzed in detail using daily surface and 850-mb data, with emphasis on possible relationships to seasonal changes in vegetation. It is shown that the warm season dip in the DTR deepens and widens from north to south across the study area, in accordance with a lengthening of the growing season. Furthermore, the dip is particularly prominent in the annual march of the DTR on mostly sunny days, indicating that seasonal variations in cloudiness are not responsible for this feature. The climatologies of daily maximum and daily minimum temperatures are found to be very different from each other: the former flattens out after the springtime peak in the DTR whereas the latter exhibits a pronounced mid-to-late summer maximum. These findings suggest that, by inhibiting daytime surface heating, evapotranspiration from vegetation contributes significantly to the dip in the DTR during the warm season in the eastern United States. [ABSTRACT FROM AUTHOR]

When long integrations of climate models forced by observed boundary conditions are compared against observations, differences appear that have spatial and temporal coherence. These differences are due to several causes, the largest of which are fundamental model errors and the internal variability inherent in a GCM integration. Uncertainties in the observations themselves are small in comparison. The present paper constitutes a first attempt to compare the time dependence of these spatial difference patterns with the time dependence of simulated spatial patterns of climate change associated with anthropogenic sources. The analysis procedure was to project the model minus observed near-surface temperature difference fields onto estimates of the anthropogenic "signal" (in this case the response to greenhouse-gas and sulfate-aerosol forcing). The temporal behavior of this projection was then compared with the estimated temporal evolution of the anthropogenic signal. Such comparisons were performed on timescales of 10, 20, and 30 yr. For trends of only 10 yr in length, the model minus observed spatial difference patterns are of the same magnitude and have the same time rate of change as the expected anthropogenic signal. In the case of 20- and 30-yr trends, the prospects are favorable for discriminating between temperature changes due to anthropogenic signal changes and changes associated with model minus observed difference structures. This suggests that attempts to quan-titatively detect anthropogenic climate change should be based on temporal samples of at least several decades in length. This study also shows the importance of distinguishing between purely statistical detection and what the authors term practical prediction. It is found that the results of the detection analysis are sensitive to the spatial resolution at which it is performed: for the specific case of near-surface temperature, higher spatial resolution improves ability to discri. [ABSTRACT FROM AUTHOR]

This article discusses climate change and hurricanes. Papers that appeared in the scholarly journals "Nature" and "Science" during the summer of 2005 raised the question of global warming being the cause of the record-breaking hurricane season of 2005. The article examines that argument, noting that many scientists believe that human-induced climate warming is indeed real. Also discussed is the increasing intensity of hurricanes since 1975, as well as the rise in seas surface temperatures and greenhouse gases in the atmosphere.