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Recent observations and model studies of the earth’s radiative energy balance have focused attention on the earth’s top of atmosphere (TOA) energy balance. This is the balance between the shortwave energy absorbed by the earth, which is represented by a spatially and temporally averaged absorbed flux , and the emitted longwave energy, which is represented by the corresponding averaged emitted flux . The TOA average net flux FN is defined as the difference between the two over the averaged area and time, which may be a local, regional, or global average. A global nonzero net flux represents a measure of imbalance between the energy being absorbed and emitted by the earth for the time interval in question. It is of interest to ask what the natural variability of the net flux might be and whether, during times of climate change, signals of important climate change processes might be detected against this natural background variation; examples of these signals include evidence of ocean heat storage, the effects of El Niño, and the radiative effects of volcanic eruptions. In this paper, the authors review the significance of the net flux, survey the observational evidence from a range of satellite instruments over several decades, and analyze some of the most recent observations from the Clouds and the Earth’s Radiant Energy System (CERES) program to determine what signals and what natural variability might be expected in the TOA net flux. Based on this analysis, the use of broadband radiation measurements for global climate change studies can be assessed. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) air pollution is a serious public health issue for the United States. While there is a growing body of evidence that climate change will partially counter the effectiveness of future precursor emission reductions to reduce ozone (O3) air pollution, the links between PM and climate change are more complex and less understood. This paper discusses what we currently understand about the potential sensitivity of PM episodes to climate-change-related shifts in air pollution meteorology, in the broader context of the emissions and atmospheric chemistry drivers of PM. For example, initial studies have focused largely on annual average concentrations of inorganic aerosol species. However, the potential for future changes in the occurrence of PM episodes, and their underlying meteorological drivers, are likely more important to understand and remain highly uncertain. In addition, a number of other poorly understood factors interact with these likely critical meteorological changes. These include changes in emissions from wildfires, as well as atmospheric processing of organic aerosol precursor chemicals. More work is needed to support the management of the health and environmental risks of climate-induced changes in PM. We suggest five priorities for the research community to address based on the current state of the literature. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the precipitation climate signal in Europe emerging from a simulation of heterogeneous regional climate models (RCMs) using five observational datasets as the reference for present day climate conditions. Current climate simulations, as well as those from the A2 family of scenarios from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES-A2), from eight RCMs involved in the Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects (PRUDENCE) project have been cross-compared with data from the Climate Research Unit (CRU), the Global Precipitation Climatology Project (GPCP), the Global Precipitation Climatology Centre (GPCC), the Climate Prediction Center (CPC), and the CPC Merged Analysis of Precipitation (CMAP) databases for Europe. The RCMs used are HIRHAM, the Climate High Resolution Model (CHRM), the Rossby Centre Atmosphere–Ocean (RCAO) model, the GKSS Climate Version of the Local Model (CLM), the Hadley Center RCM (HadRM3H), the Atmospheric Hydrostatic Regional Model (REMO), the Prognostic Model at the Mesoscale (PROMES), and the regional coupled ocean–atmosphere–ice model (RACMO). The comparison shows that the climate signal has to be interpreted depending on the reference data used. Although each validation dataset has its own relative merits and shortcomings, it is known that all of the datasets present variable uncertainties and error sources, which impedes consideration of a single dataset as the only valid representation of actual precipitation. Hence, it is suggested that a robust joint estimate of changes in future precipitation might include the uncertainties of both the RCMs and those of the observational datasets. After accounting for the difference between observed and simulated precipitation in the present climate, the analysis of such joint estimates reveals significant agreement in the climate signal for most of Europe. This lends confidence to the idea that the RCMs are able to correctly simulate future changes in precipitation. [ABSTRACT FROM AUTHOR]

The climate observations at Orcadas represent the only southern high-latitude site where data span more than a century, and its daily measurements are presented for the first time in this paper. Although limited to a single station, the observed warming trends are among the largest found anywhere on the earth, facilitating the study of changes in extreme temperatures as well as averages. Factors that may influence Antarctic climate include natural variability; changes in greenhouse gases; and, since about the mid-1970s, the development of the ozone hole. The seasonality of observed warming and its temporal evolution during the century are both key for interpretations of Antarctic climate change. No statistically significant climate trends are observed at Orcadas from 1903 to 1950. However, statistically significant warming is evident at Orcadas throughout all four seasons of the year since 1950. Particularly in austral fall and winter, the warming of the cold extremes (coldest 5% and 10% of days) substantially exceeds the warming of the mean or of the warmest days, providing a key indicator for cold season Antarctic climate change studies. Trends in the summer season means and extremes since 1970 are approximately twice as large as those observed earlier, supporting suggestions of additional regional warming in that season because of the effects of ozone depletion on the circulation. Further, in the spring and summer seasons, significant mean warming also occurred prior to the development of the Antarctic ozone hole (i.e., 1950–70), supporting an important role for processes other than ozone depletion, such as greenhouse gas increases, for the climate changes. [ABSTRACT FROM AUTHOR]

Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration–carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate–carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), climate–carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration–carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate–carbon feedback and more uncertain. The concentration–carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon–climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration–carbon and climate–carbon feedbacks do not combine linearly, and the concentration–carbon feedback is dependent on scenario and time. [ABSTRACT FROM AUTHOR]

Efforts are being made to develop new paradigms for climate change adaptation policy at both the national and the international levels. However, progress in vulnerability and adaptation research has not been matched by advancement on practical policy initiatives. The complexity of the challenge to develop methods and means to support adaptation to climate change necessitates a diversity of approaches. This diversity is healthy, and yet it is possible to define some key characteristics and tools that can promote practical outcomes. In this paper, some methodologies that have proved successful are reviewed. These include a mapping of contextual circumstances, an appreciation for multiple perspectives, and the importance of a "boundary object" in forging strong interactions among project participants. Further, a toolkit of approaches for natural scientists is presented. This toolkit can be used to organize work in collaboration with stakeholders and other participants and can help overcome barriers to a meaningful contribution to the policy process. Fundamentally, this challenge will require approaches that are more grounded in meaning, narration, and reflection. [ABSTRACT FROM AUTHOR]

In 2004 a new aspirated surface air temperature system was officially deployed nationally in the U.S. Climate Reference Network (USCRN) commissioned by the National Oceanic and Atmospheric Administration. The primary goal of the USCRN is to provide future long-term and high-quality homogeneous observations of surface air temperature and precipitation that can be coupled to past long-term observations for the detection and attribution of present and future climate change. In this paper two precision air temperature systems are included for evaluating the new USCRN air temperature system based on a 1-yr side-by-side field comparison. The measurement errors of the USCRN temperature sensor are systematically analyzed, and the components of error attributable to the datalogger, lead wires, fixed resistors, and the temperature coefficient of the resistors are presented. Although the current configuration is adequate, a more desirable configuration of USCRN temperature sensor coupled with the datalogger is proposed as a means of further reducing the uncertainty for the USCRN temperature measurement. [ABSTRACT FROM AUTHOR]

Projections of human-induced climate change impacts arising from the emission of atmospheric chemical constituents such as carbon dioxide typically utilize multiple integrations (or ensembles) of numerous numerical climate change models to arrive at multimodel ensembles from which mean and median values and probabilities can be inferred about the response of various components of the observed climate system. Some responses are considered reliable in as much as the simulated responses show consistency within ensembles and across models. Other responses—particularly at regional levels and for certain parameters such as precipitation—show little intermodel consistency even in the sign of the projected climate changes. The authors’ results show that in these regions the consistency in the sign of projected precipitation variations is greater for intramodel runs (e.g., runs from the same model) than intermodel runs (e.g., runs from different models), indicating that knowledge of the internal “dynamics” of the climate system can provide additional skill in making projections of climate change. Given the consistency provided by the governing dynamics of the model, the authors test whether persistence from an individual model trajectory serves as a good predictor for its own behavior by the end of the twenty-first century. Results indicate that, in certain regions where intermodel consistency is low, the short-term trends of individual model trajectories do provide additional skill in making projections of long-term climate change. The climate forcing for which this forecast skill becomes relatively large (e.g., correct in 75% of the individual model runs) is equivalent to the anthropogenic climate forcing imposed over the past century, suggesting that observed changes in precipitation in these regions can provide guidance about the direction of future precipitation changes over the course of the next century. [ABSTRACT FROM AUTHOR]

Assessment of the upper-ocean (0-700 m) heat content (OHC) is a key task for monitoring climate change. However, irregular spatial and temporal distribution of historical subsurface observations has induced uncertainties in OHC estimation. In this study, a new source of uncertainties in calculating OHC due to the insufficiency of vertical resolution in historical ocean subsurface temperature profile observations was diagnosed. This error was examined by sampling a high-vertical-resolution climatological ocean according to the depth intervals of in situ subsurface observations, and then the error was defined as the difference between the OHC calculated by subsampled profiles and the OHC of the climatological ocean. The obtained resolution-induced error appeared to be cold in the upper 100 m (with a peak of approximately −0.1°C), warm within 100-700 m (with a peak of ~0.1°C near 180 m), and warm when averaged over 0-700-m depths (with a global average of ~0.01°-0.025°C, ~1-2.5 × 1022 J). Geographically, it showed a warm bias within 30°S-30°N and a cold bias at higher latitudes in both hemispheres, the sign of which depended on the concave or convex shape of the vertical temperature profiles. Finally, the authors recommend maintaining an unbiased observation system in the future: a minimal vertical depth bin of 5% of the depth was needed to reduce the vertical-resolution-induced bias to less than 0.005°C on global average (equal to Argo accuracy). [ABSTRACT FROM AUTHOR]

The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models' spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM-which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios-with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information. [ABSTRACT FROM AUTHOR]

Regional climate model (RCM) simulations, driven by low and high climate-sensitivity coupled general circulation models (CGCMs) under various future emissions scenarios, were compared to projected changes in heat wave characteristics. The RCM downscaling reduces the CGCM biases in heat wave threshold temperature by a factor of 2, suggesting a higher credibility in the future projections. All of the RCM simulations suggest that there is a high probability of heat waves of unprecedented severity by the end of the twenty-first century if a high emissions path is followed. In particular, the annual 3-day heat wave temperature increases generally by 3°–8°C; the number of heat wave days increases by 30–60 day yr−1 over much of the western and southern United States with slightly smaller increases elsewhere; the variance spectra for intermediate, 3–7 days (prolonged, 7–14 days), temperature extremes increase (decrease) in the central (western) United States. If a lower emissions path is followed, then the outcomes range from quite small changes to substantial increases. In all cases, the mean temperature climatological shift is the dominant change in heat wave characteristics, suggesting that adaptation and acclimatization could reduce effects. [ABSTRACT FROM AUTHOR]

Controls on the sensitivity of mountain snowpack accumulation to climate warming ( λS) are investigated. This is accomplished using two idealized, physically based models of mountain snowfall to simulate snowpack accumulation for the Cascade Mountains under current and warmed climates. Both models are forced from sounding observations. The first model uses the linear theory (LT) model of orographic precipitation to predict precipitation as a function of the incoming flow characteristics and uses the sounding temperatures to estimate the elevation of the rain–snow boundary, called the melting level (ML). The second “ML model” uses only the ML from the sounding and assumptions of uniform and constant precipitation. Both models simulate increases in precipitation intensity and elevated storm MLs under climate warming. The LT model predicts a 14.8%–18.1% loss of Cascade snowfall per degree of warming, depending on the vertical structure of the warming. The loss of snowfall is significantly greater, 19.4%–22.6%, if precipitation increases are neglected. Comparing the two models shows that the predominant control on λS is the relationship between the distribution of storm MLs and the distribution of topographic area with elevation. Although increases in precipitation due to warming may act to moderate λS, the loss of snow accumulation area profoundly limits the ability of precipitation increases to maintain the snowpack under substantial climate warming (beyond 1°–2°C). Circulation changes may act to moderate or exacerbate the loss of mountain snowpack under climate change via impacts on orographic precipitation enhancement. [ABSTRACT FROM AUTHOR]

The influence of surface thermohaline forcing on the variability of the Atlantic meridional overturning circulation (MOC) at mid–high latitudes is investigated using output from three Intergovernmental Panel on Climate Change (IPCC) coupled climate models. The method employed is an extension of the surface-forced streamfunction approach, based on water mass transformation theory, used in an earlier study by Marsh (2000). The maximum value of the MOC at 48°N is found to have a significant lagged relationship with the maximum surface-forced streamfunction in the region north of 48°N with a surface density greater than σ0 = 27.5 kg m-3. This correlation peaks when the index of the surface-forced streamfunction leads the MOC by 2–4 yr, depending on the coupled model considered. A method for estimating the MOC variability solely from the surface forcing fields is developed and found to be in good agreement with the actual model MOC variability in all three of the models considered when a past averaging window of 10 yr is employed. This method is then applied with NCEP–NCAR reanalysis surface flux fields for the period 1949–2007 to reconstruct MOC strength over 1958–2007. The reconstructed MOC shows considerable multidecadal variability but no discernible trend over the modern observational era. [ABSTRACT FROM AUTHOR]

Weather Research and Forecasting (WRF) model simulations are performed over Russia for July and December 2005, 2006, and 2007 to create a “dataset” to assess the impact of network density and design on regional averages. Based on the values at all WRF grid points, regional averages for various quantities are calculated for 2.8° × 2.8° areas as the “reference.” Regional averages determined based on 40 artificial networks and 411 “sites” that correspond to the locations of a real network are compared with the reference regional averages. The 40 networks encompass 10 networks of 500, 400, 200, or 100 different randomly taken WRF grid points as sites. The real network’s site distribution misrepresents the landscape. This misrepresentation leads to errors in regional averages that show geographical and temporal trends for most quantities: errors are lower over shores of large lakes than coasts and lowest over flatland followed by low and high mountain ranges; offsets in timing occur during frontal passages when several sites are passed at nearly the same time. Generally, the real network underestimates regional averages of sea level pressure, wind speed, and precipitation over Russia up to 4.8 hPa (4.8 hPa), 0.7 m s-1 (0.5 m s-1), and 0.2 mm day-1 and overestimates regional averages of 2-m temperature, downward shortwave radiation, and soil temperature over Russia up to 1.9 K (1.4 K), 19 W m-2 (14 W m-2), and 1.5 K (1.8 K) in July (December). The low density of the ten 100-site networks causes difficulties for sea level pressure. Regional averages obtained from the 30 networks with 200 or more randomly distributed sites represent the reference regional averages, trends, and variability for all quantities well. [ABSTRACT FROM AUTHOR]

A novel approach is presented to objectively identify regional patterns of climate variability within the state of California using principal component analysis on monthly precipitation and temperature data from a network of 195 climate stations statewide and an ancillary gridded database. The confluence of large-scale circulation patterns and the complex geography of the state result in 11 regional modes of climate variability within the state. A comparison between the station and gridded analyses reveals that finescale spatial resolution is needed to adequately capture regional modes in complex orographic and coastal settings. Objectively identified regions can be employed not only in tracking regional climate signatures, but also in improving the understanding of mechanisms behind regional climate variability and climate change. The analysis has been incorporated into an operational tool called the California Climate Tracker. [ABSTRACT FROM AUTHOR]

To date, neither observational studies nor direct climate model simulations have been able to document trends in the frequency or severity of deep moist convection associated with global climate change. The lack of such evidence is not unexpected as the observational record is insufficiently long and computational limitations prevent modeling at the scales necessary to simulate explicitly such phenomena. Nonetheless, severe deep moist convection represents an important aspect of regional climate, particularly in the central United States, where damage, injuries, and fatalities are a frequent result of such phenomena. Accordingly, any comprehensive assessment of the regional effects of climate change must account for these effects. In this work, the authors present a “perfect prog” approach to estimating the potential for surface-based convective initiation and severity based upon the large-scale variables well resolved by climate model simulations. This approach allows for the development of a stable estimation scheme that can be applied to any climate model simulation, presently and into the future. The scheme is applied for the contiguous United States using the output from the Parallel Climate Model, with the Intergovernmental Panel on Climate Change third assessment A2 (business as usual) as input. For this run, relative to interannual variability, the potential frequency of deep moist convection does not change, but the potential for severe convection is found to increase east of the Rocky Mountains and most notably in the “tornado alley” region of the U.S. Midwest. This increase in severe potential is mostly tied to increases in thermodynamic instability as a result of ongoing warm season surface warming and moistening. Finally, approaches toward improving such estimation methods are briefly discussed. [ABSTRACT FROM AUTHOR]

The manner in which monthly mean sea surface temperature anomalies (SSTAs) show enhanced variance at the annual period in the extratropics (an annual peak in the variance spectrum) is illustrated by observations and model simulations. A mechanism, related to the reemergence of winter SST anomalies, is proposed to explain the annual peak in SST spectrum. The idea is supported by the analysis of a hierarchy of models, including Intergovernmental Panel on Climate Change model simulations. The results of the model experiments further suggest that the annual peak is either weak or absent if decadal SST variability is forced by local air–sea interaction. However, if ocean subsurface temperature variability forces decadal SST variability, the annual peak is much stronger. Strong annual peaks may therefore be seen as an indication of ocean-forced decadal SST variability in the extratropics. [ABSTRACT FROM AUTHOR]

Increasing surface temperatures are expected to result in longer growing seasons. An optimal detection analysis is carried out to assess the significance of increases in the growing season length during 1950–99, and to measure the anthropogenic component of the change. The signal is found to be detectable, both on global and continental scales, and human influence needs to be accounted for if it is to be fully explained. The change in the growing season length is found to be asymmetric and largely due to the earlier onset of spring, rather than the later ending of autumn. The growing season length, based on exceedence of local temperature thresholds, has a rate of increase of about 1.5 days decade-1 over the observation area. Local variations also allow for negative trends in parts of North America. The analysis suggests that the signal can be attributed to the anthropogenic forcings that have acted on the climate system and no other forcings are necessary to describe the change. Model projections predict that under future climate change the later ending of autumn will also contribute significantly to the lengthening of the growing season, which will increase in the twenty-first century by more than a month. Such major changes in seasonality will affect physical and biological systems in several ways, leading to important environmental and socioeconomic consequences and adaptation challenges. [ABSTRACT FROM AUTHOR]

WMO-recommended 30-yr normals are no longer generally useful for the design, planning, and decision-making purposes for which they were intended. They not only have little relevance to the future climate, but are often unrepresentative of the current climate. The reason for this is rapid global climate change over the last 30 yr that is likely to continue into the future. It is demonstrated that simple empirical alternatives already are available that not only produce reasonably accurate normals for the current climate but also often justify their extrapolation to several years into the future. This result is tied to the condition that recent trends in the climate are approximately linear or have a substantial linear component. This condition is generally satisfied for the U.S. climate-division data. One alternative [the optimal climate normal (OCN)] is multiyear averages that are not fixed at 30 yr like WMO normals are but rather are adapted climate record by climate record based on easily estimated characteristics of the records. The OCN works well except with very strong trends or longer extrapolations with more moderate trends. In these cases least squares linear trend fits to the period since the mid-1970s are viable alternatives. An even better alternative is the use of “hinge fit” normals, based on modeling the time dependence of large-scale climate change. Here, longer records can be exploited to stabilize estimates of modern trends. Related issues are the need to avoid arbitrary trend fitting and to account for trends in studies of ENSO impacts. Given these results, the authors recommend that (a) the WMO and national climate services address new policies for changing climate normals using the results here as a starting point and (b) NOAA initiate a program for improved estimates and forecasts of official U.S. normals, including operational implementation of a simple hybrid system that combines the advantages of both the OCN and the hinge fit. [ABSTRACT FROM AUTHOR]

The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups. [ABSTRACT FROM AUTHOR]

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models. [ABSTRACT FROM AUTHOR]

Four climate system models are chosen here for an analysis of ENSO amplitude changes in 4 × CO2 climate change projections. Despite the large changes in the tropical Pacific mean state, the changes in ENSO amplitude are highly model dependant. To investigate why similar mean state changes lead to very different ENSO amplitude changes, the characteristics of sea surface temperature anomaly (SSTA) variability simulated in two coupled general circulation models (CGCMs) are analyzed: the Meteorological Research Institute (MRI) and Geophysical Fluid Dynamics Laboratory (GFDL) models. The skewed distribution of tropical Pacific SSTA simulated in the MRI model suggests the importance of nonlinearities in the ENSO physics, whereas the GFDL model lies in the linear regime. Consistent with these differences in ENSO regime, the GFDL model is insensitive to the mean state changes, whereas the MRI model is sensitive to the mean state changes associated with the 4 × CO2 scenario. Similarly, the low-frequency modulation of ENSO amplitude in the GFDL model is related to atmospheric stochastic forcing, but in the MRI model the amplitude modulation is insensitive to the noise forcing. These results suggest that the understanding of changes in ENSO statistics among various climate change projections is highly dependent on whether the model ENSO is in the linear or nonlinear regime. [ABSTRACT FROM AUTHOR]

A method for the detection of a bounded weak-echo region (BWER) within a storm structure that can help in the prediction of severe weather phenomena is presented. A fuzzy rule–based approach that takes care of the various uncertainties associated with a radar image containing a BWER has been adopted. The proposed technique automatically finds some interpretable (fuzzy) rules for classification of radar data related to BWER. The radar images are preprocessed to find subregions (or segments) that are suspected candidates for BWERs. Each such segment is classified into one of three possible cases: strong BWER, marginal BWER, or no BWER. In this regard, spatial properties of the data are being explored. The method has been tested on a large volume of data that are different from the training set, and the performance is found to be very satisfactory. It is also demonstrated that an interpretation of the linguistic rules extracted by the system described herein can provide important characteristics about the underlying process. [ABSTRACT FROM AUTHOR]

The ability of coupled GCMs to correctly simulate the climatology and a prominent mode of variability of the West African monsoon is evaluated, and the results are used to make informed decisions about which models may be producing more reliable projections of future climate in this region. The integrations were made available by the Program for Climate Model Diagnosis and Intercomparison for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The evaluation emphasizes the circulation characteristics that support the precipitation climatology, and the physical processes of a “rainfall dipole” variability mode that is often associated with dry conditions in the Sahel when SSTs in the Gulf of Guinea are anomalously warm. Based on the quality of their twentieth-century simulations over West Africa in summer, three GCMs are chosen for analysis of the twenty-first century integrations under various assumptions about future greenhouse gas increases. Each of these models behaves differently in the twenty-first-century simulations. One model simulates severe drying across the Sahel in the later part of the twenty-first century, while another projects quite wet conditions throughout the twenty-first century. In the third model, warming in the Gulf of Guinea leads to more modest drying in the Sahel due to a doubling of the number of anomalously dry years by the end of the century. An evaluation of the physical processes that cause these climate changes, in the context of the understanding about how the system works in the twentieth century, suggests that the third model provides the most reasonable projection of the twenty-first-century climate. [ABSTRACT FROM AUTHOR]

Extratropical and tropical transient storm tracks are investigated from the perspective of feature tracking in the ECHAM5 coupled climate model for the current and a future climate scenario. The atmosphere-only part of the model, forced by observed boundary conditions, produces results that agree well with analyses from the 40-yr ECMWF Re-Analysis (ERA-40), including the distribution of storms as a function of maximum intensity. This provides the authors with confidence in the use of the model for the climate change experiments. The statistical distribution of storm intensities is virtually preserved under climate change using the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario until the end of this century. There are no indications in this study of more intense storms in the future climate, either in the Tropics or extratropics, but rather a minor reduction in the number of weaker storms. However, significant changes occur on a regional basis in the location and intensity of storm tracks. There is a clear poleward shift in the Southern Hemisphere with consequences of reduced precipitation for several areas, including southern Australia. Changes in the Northern Hemisphere are less distinct, but there are also indications of a poleward shift, a weakening of the Mediterranean storm track, and a strengthening of the storm track north of the British Isles. The tropical storm tracks undergo considerable changes including a weakening in the Atlantic sector and a strengthening and equatorward shift in the eastern Pacific. It is suggested that some of the changes, in particular the tropical ones, are due to an SST warming maximum in the eastern Pacific. The shift in the extratropical storm tracks is shown to be associated with changes in the zonal SST gradient in particular for the Southern Hemisphere. [ABSTRACT FROM AUTHOR]

One of the major uncertainties in the ability to predict future climate change, and hence its impacts, is the lack of knowledge of the earth’s climate sensitivity. Here, data are combined from the 1985–96 Earth Radiation Budget Experiment (ERBE) with surface temperature change information and estimates of radiative forcing to diagnose the climate sensitivity. Importantly, the estimate is completely independent of climate model results. A climate feedback parameter of 2.3 ± 1.4 W m-2 K-1 is found. This corresponds to a 1.0–4.1-K range for the equilibrium warming due to a doubling of carbon dioxide (assuming Gaussian errors in observable parameters, which is approximately equivalent to a uniform “prior” in feedback parameter). The uncertainty range is due to a combination of the short time period for the analysis as well as uncertainties in the surface temperature time series and radiative forcing time series, mostly the former. Radiative forcings may not all be fully accounted for; however, an argument is presented that the estimate of climate sensitivity is still likely to be representative of longer-term climate change. The methodology can be used to 1) retrieve shortwave and longwave components of climate feedback and 2) suggest clear-sky and cloud feedback terms. There is preliminary evidence of a neutral or even negative longwave feedback in the observations, suggesting that current climate models may not be representing some processes correctly if they give a net positive longwave feedback. [ABSTRACT FROM AUTHOR]

An evaluation of three hypothesis test statistics that are commonly used in the detection of undocumented changepoints is described. The goal of the evaluation was to determine whether the use of multiple tests could improve undocumented, artificial changepoint detection skill in climate series. The use of successive hypothesis testing is compared to optimal approaches, both of which are designed for situations in which multiple undocumented changepoints may be present. In addition, the importance of the form of the composite climate reference series is evaluated, particularly with regard to the impact of undocumented changepoints in the various component series that are used to calculate the composite. In a comparison of single test changepoint detection skill, the composite reference series formulation is shown to be less important than the choice of the hypothesis test statistic, provided that the composite is calculated from the serially complete and homogeneous component series. However, each of the evaluated composite series is not equally susceptible to the presence of changepoints in its components, which may be erroneously attributed to the target series. Moreover, a reference formulation that is based on the averaging of the first-difference component series is susceptible to random walks when the composition of the component series changes through time (e.g., values are missing), and its use is, therefore, not recommended. When more than one test is required to reject the null hypothesis of no changepoint, the number of detected changepoints is reduced proportionately less than the number of false alarms in a wide variety of Monte Carlo simulations. Consequently, a consensus of hypothesis tests appears to improve undocumented changepoint detection skill, especially when reference series homogeneity is violated. A consensus of successive hypothesis tests using a semihierarchic splitting algorithm also compares favorably to optimal solutions, even when changepoints are not hierarchic. [ABSTRACT FROM AUTHOR]

There is a growing demand for regional-scale climate predictions and assessments. Quantifying the impacts of uncertainty in initial conditions and lateral boundary forcing data on regional model simulations can potentially add value to the usefulness of regional climate modeling. Results from a regional model depend on the realism of the driving data from either global model outputs or global analyses; therefore, any biases in the driving data will be carried through to the regional model. This study used four popular global analyses and achieved 16 driving datasets by using different interpolation procedures. The spread of the 16 datasets represents a possible range of driving data based on analyses to the regional model. This spread is smaller than typically associated with global climate model realizations of the Arctic climate. Three groups of 16 realizations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in an Arctic domain, varying both initial and lateral boundary conditions, varying lateral boundary forcing only, and varying initial conditions only. The response of monthly mean atmospheric states to the variations in initial and lateral driving data was investigated. Uncertainty in the regional model is induced by the interaction between biases from different sources. Because of the nonlinearity of the problem, contributions from initial and lateral boundary conditions are not additive. For monthly mean atmospheric states, biases in lateral boundary conditions generally contribute more to the overall uncertainty than biases in the initial conditions. The impact of initial condition variations decreases with the simulation length while the impact of variations in lateral boundary forcing shows no clear trend. This suggests that the representativeness of the lateral boundary forcing plays a critical role in long-term regional climate modeling. The extent of impact of the driving data uncertainties on regional climate modeling is variable dependent. For some sensitive variables (e.g., precipitation, boundary layer height), even the interior of the model may be significantly affected. [ABSTRACT FROM AUTHOR]

A box inverse of the World Ocean Circulation Experiment A10 (30°S) and A11 (nominally 45°S) sections in the South Atlantic Ocean was undertaken. The authors find a heat flux across A10 of 0.22 ± 0.08 PW, consistent with previous studies, and a heat flux of 0.43 ± 0.08 PW across A11. The A11 heat flux is lower than some previous analyses of this section but implies a plausible oceanic heat convergence (heat loss to the atmosphere) of 0.21 ± 0.10 PW. The difference is principally due to adding a cyclonic component to the circulation in the Cape Basin. As compared with the solution of other studies, the anticyclonic circulation in the surface and intermediate water of the subtropical gyre is weakened. The circulation of the deep water is cyclonic rather than anticyclonic; this is in better agreement with previously published circulation schemes based on examination of water properties. A southward freshwater flux of 0.7 Sv (1 Sv ≡ 106 m3 s-1) at A11, consistent with previous inverse studies, is still inconsistent with the net Atlantic evaporation inferred from integrated surface climatologies. Results suggest a small gain of freshwater (0.2 ± 0.1 Sv) between the sections. [ABSTRACT FROM AUTHOR]

The Northern Hemisphere annular mode (NAM), North Atlantic Oscillation (NAO), and Aleutian low (AL) are known to be the most prominent components of Northern Hemisphere (NH) near-surface climate variability. In a tremendous number of studies, the impact of these circulation features on regional climate has been demonstrated. More recently, research has gone into the connection between the NAO and NAM and into the physical meaning of the latter. However, the relevance of those circulation modes for climatological issues may also be inferred from another nondynamical point of view: their statistical relationship to various climate parameters. This study comprises two steps: 1) qualifying and quantifying the relative importance of NH circulation modes with respect to twentieth-century near-surface temperature and precipitation, using stepwise multiple regression with cross validation; and 2) using predictor–predictand relationships to access the contributions of each circulation mode to regional climate change in the middle of the twenty-first century, given multimodel predictions of the circulation modes' responses to increasing greenhouse gas (GHG) and sulfate aerosol (SUL) concentrations. Altogether, the NAM, NAO, and AL account locally for up to 75% of the total interannual temperature and rainfall variability over NH continents. Over the major part of the NH, the NAM appears to be the most important predictor. In some parts of the North Atlantic, temperature and rainfall are more closely linked to the NAO, while the North Pacific is clearly dominated by the AL dynamics. In general, the NAO and AL have a more regionally confined influence. Climate change experiments mostly predict an intensification of the NAM and AL under GHG+SUL forcing, while the NAO response is much less consistent with different models and generally undergoes no long-term changes. This leads to substantial contributions to temperature and rainfall anomalies, especially over the NH landmasses. Temperature changes amount to ±1 K over large parts of Russia, North America, and the North Pacific. The major precipitation changes occur over the North Pacific, the North Atlantic, and Scandinavia. This circulation-induced contribution accounts for a considerable part of total expected change in these regions. Given its distinct trend, the NAM plays the main role, except over the Pacific Ocean and North America, where the AL is driving regional climate anomalies. Thus, whether physically relevant or not, the NAM is an appropriate statistical indicator of NH regional climate change. [ABSTRACT FROM AUTHOR]

As radiation plays a key role in the determination of the near-surface thermal environment, great accuracy is required in the computation of radiative fluxes, especially because a small error in the fluxes can lead to large errors in estimated cooling rates. A new code that employs a novel numerical scheme for making precise estimates of longwave fluxes and cooling rates near the surface of the earth, for arbitrary surface emissivities, is presented here. The code is a development of the infrared band model of Chou, Ridgway, and Yan. Unacceptable oscillations found near the surface in the cooling rates provided by this code have been overcome through the new numerical scheme. The new code gives results in excellent agreement with available results from Intercomparison of Radiation Codes used in Climate Models (ICRCCM) test cases and with the line-by-line calculations of Clough, Iacono, and Moncet. The code has no restriction on the number of grid points, yields fluxes accurate to a prescribed tolerance, and permits a discontinuity in temperature at the surface (although this is not used in the results presented). The computing times are comparable (for given accuracy) to those demanded by current codes in use elsewhere. It is found that, as surface emissivity ℇ[sub g] departs from unity, the cooling rate rises dramatically near the surface, reaching values as high as nearly 40 K day[sup -1] at ℇ[sub g] = 0.8 in the midlatitude summer atmosphere, and the effect of the surface is noticeable at heights of up to about 1 km. An analysis of spectral distribution shows that, when the surface is not radiatively black, the major contributions to near-surface cooling rates (due to water vapor) come from the two wavenumber bands, 340–540 cm[sup -1] and 1215–1380 cm[sup -1] (located on either side of the atmospheric window), in which both absorption and radiative flux are significantly high. [ABSTRACT FROM AUTHOR]

A linear shallow water model is used to simulate the evolution of mixed Rossby–gravity (MRG) waves in background states representative of the convective phase of the Madden–Julian oscillation (MJO). Initial MRG wave structures are obtained analytically. The MJO basic state is defined by the steady response of the tropical atmosphere to localized heating. Results from the simulations reveal that variations in the background flow play a significant role in the evolution of the MRG waves. When the basic state is symmetric about the equator, the MRG wave amplifies within the convergent region of the background flow and the ensuing development remains symmetric. When the heating is asymmetric, both the basic state and the MRG wave evolution exhibit significant asymmetries. Prominent features of this simulation are the development and growth of a series of small-scale, off-equatorial eddies that resemble tropical-depression-type disturbances. The results suggest that a persistent large-scale heating that is asymmetric with respect to the equator may lead to the growth of off-equatorial disturbances from an equatorial mode. These disturbances, approximately 1000–2000 km in scale, are considerably smaller than the initial wavelength of the MRG wave. It is suggested that the cyclonic elements among them could serve as seedlings for tropical cyclones. This process may be particularly relevant to cyclogenesis in the tropical western Pacific, a region where the MJO and MRG waves are frequently observed. [ABSTRACT FROM AUTHOR]

Using laboratory experimental data taken from a temperature-controlled water tank, the basic features of the circulation associated with an inland urban heat island (UHI) of diameter D and surface heating rate H[sub 0] and its interaction with a sea-breeze current were investigated. When the environment was stably stratified with a buoyancy frequency N (nocturnal UHIs) and the sea breeze was absent, the UHI circulation was mainly governed by the Froude number [Fr = U/(ND)], in agreement with theoretical predictions found in literature (U was the horizontal velocity scale of the flow based on H[sub 0] and D). Furthermore, the results were in agreement with other laboratory studies and with nighttime field observations conducted in large cities. It was found that U, when calculated with H[sub 0] + H[sub L] (where H[sub L] was the surface heat flux associated with the land temperature growth occurring after sunrise), could also be employed as the velocity scale for UHI circulations that develop in statically unstable environments (daytime UHIs). Analysis of the interaction between an inland daytime UHI and a sea-breeze flow with maximum velocity U[sub B] showed that the resulting circulation was strongly dependent on the ratio U[sub B] /U and that substantial differences from cases in which urban complexes are located along the shore were present. Comparison with numerical experiments also supports these results. [ABSTRACT FROM AUTHOR]

The mean genesis environment was constructed for each of five mesoscale convective complex (MCC) population centers around the world: Africa, Australia, China, South America, and the United States. It is found that the environments are very similar and exhibit many of the same dynamic and thermodynamic structures that are present with systems in the United States. In particular, MCCs initiate within prominent baroclinic zones characterized by locally large values of lower-tropospheric vertical wind shear and convective available potential energy (CAPE). Typically, a low-level jet of air with low static stability, high equivalent potential temperature, oriented nearly perpendicular to the baroclinic zone, intrudes into the genesis region and is forced to ascend over a relatively shallow, surface-based layer of relatively cool air. Pronounced warm advection accompanied by strong lower-tropospheric veering overlays the surface-based cool layer. A local maximum in absolute humidity and a local minimum in static stability mark the favored region for formation of the convective system. Low-level convergence, upper-level divergence, and an approaching midlevel vorticity maximum associated with a weak short-wave trough are also typical of the mean genesis environment. [ABSTRACT FROM AUTHOR]

The European Centre for Medium-Range Weather Forecasts Tropical Ocean–Global Atmosphere advanced analysis was used to study the mechanisms that affect the intensity of Typhoons Flo (1990) and Gene (1990). The outflow structure, eddy momentum flux convergence, and the mean vertical wind shear were examined. The evolution of potential vorticity (PV) in the outflow layer showed low PV areas on top of both Typhoons Flo and Gene, and the low PV areas expanded as the typhoons intensified. The outflow pattern of the two typhoons was influenced by the upper-tropospheric environmental systems. The upper-level environmental features were shown to play a crucial role in the intensification of the two typhoons. The tropical upper-tropospheric trough cell east of Flo provided the outflow channel for the typhoon. The enhanced outflow, the upper-level eddy flux convergence (EFC), the low vertical wind shear, and the warm sea surface temperature provided all favorable conditions for the development of Flo. On the other hand, the intensification of Gene was associated with its interaction with an upper-level midlatitude trough. The approach of the trough produced upper-level EFC of angular momentum outside 10° lat radius, and the EFC shifted inward with time. As the EFC shifted into the vicinity of the storm core, Gene started to intensify steadily until the midlatitude trough passed over. The intensifying processes of the above cases indicate the importance of the upper-tropospheric systems to the intensity change of typhoons. The influence of upper-level environmental systems on the tropical cyclones is prominent in the low inertial stability outflow layer. However, results from the piecewise PV inversion of the upper-level environmental PV anomalies showed little evidence that the intensification of both typhoons were directly associated with the superposition of PV anomalies. [ABSTRACT FROM AUTHOR]