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Under the new background of climate change, it is very important to identify the characteristics of drought in North China. Based on the daily meteorological drought comprehensive index from 494 national meteorological stations in North China during 1961–2019, the drought processes and their intensity are identified by applying the "extreme" intensity–duration (EID) theory. Then, the stage variation characteristics of the drought trend, the average drought intensity, and the drought frequency are analyzed. The results show that among the five drought intensity indices the process maximum intensity demonstrates the greatest correlation coefficient with the disaster rate of drought in North China. Therefore, the process maximum intensity of drought is selected as the annual drought intensity to analyze the drought characteristics in North China. According to the climate warming trends, the study period is divided into three stages, that is, 1951–84 (stage I), 1985–97 (stage II), and 1998–2019 (stage III). The comprehensive results show that the drought intensity in North China has significant stage characteristics. In stage I, the drought shows an increasing trend in most parts of North China, but its average intensity is relatively weaker, with a lower severe drought frequency. The drought also shows an increasing trend in most parts in stage II, with a more significant increase rate than that in stage I, and the average drought intensity is the strongest and the severe drought frequency is the highest. In stage III, the drought shows a decreasing trend in some areas, and the average intensity is the weakest, with a lower severe drought frequency. Significance Statement: In this paper, we develop a drought intensity formula, the maximum intensity of drought, based on the "extreme" intensity–duration theory. The maximum intensity of drought was then calculated and selected as an annual drought intensity to analyze the drought characteristics in North China. We found that the annual drought intensity better captured the extremity and the patterns of drought process than that obtained with single indices and comprehensive indices. The results show a decreasing trend of drought in North China after 1998. [ABSTRACT FROM AUTHOR]

Extreme heat such as that seen in the United States and Europe in summer 2022 can have significant impacts on human health and infrastructure. The Occupational Safety and Health Administration (OSHA) and the U.S. Army use wet-bulb globe temperature (WBGT) to quantify the impact of heat on workers and inform decisions on workload. WBGT is a weighted average of air temperature, natural wet-bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat-related disruptions. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with a peak WBGT about 1°F higher. The high- and extreme-risk categories in the southern U.S. Great Plains (USGP) have increased from 5 days per year to 15 days from 1960 to 2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery. [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]

This paper describes the assumptions, equations, and procedures of the RheaG weather generator algorithm (WGA). RheaG was conceived for the generation of robust daily meteorological time series, whether in static or transient climate conditions. Here we analyze its performance in four Iberian locations—Bilbao, Barcelona, Madrid, and Sevilla—with differentiated climate characteristics. To validate the RheaG WGA, we compared observed and generated meteorological time series' statistical properties of precipitation, maximum temperature, and minimum temperature for all four locations. We also compared observed and simulated rain events spell length probabilities in all four locations. Finally, RheaG includes two weather generation procedures: one in which monthly mean values for meteorological variables are unconstrained and one in which they are constrained according to a predefined baseline climate variability. Here, we compare the two weather generation procedures included in RheaG using the observed data from Barcelona. Our results present a high agreement in the statistical properties and the rain spell length probabilities between observed and generated meteorological time series. Our results show that RheaG accurately reproduces seasonal patterns of the observed meteorological time series for all four locations, and it is even able to differentiate two climatic seasons in Bilbao that are also present in the observed data. We find a trade-off between generation procedures in which the unconstrained procedure better reproduces the variability of monthly and yearly precipitation than the constrained one, but the constrained procedure is able to keep the same climatic signal across meteorological time series. Thus, the first procedure is more accurate, but the latter is able to maintain spatial autocorrelation among generated meteorological time series. [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]

This paper investigates the mean spatial features of the winds in the Mediterranean and Black Seas using the wind fields observed by the SeaWinds scatterometer. Five years (2000–04) of data have been analyzed on annual and seasonal basis, with particular attention paid to the meso- and local scales. The fields show the existence of structured regional wind systems—in particular, the mistral in the western Mediterranean and the etesians in the Levantine Basin, which are characterized, respectively, by high wind variability and moderate steadiness and by low wind variability and high steadiness. Estimated seasonal mean wind stress τ fields show that the values falling in the top range 0.15 < τ < 0.20 N m-2 affect a large portion of the Mediterranean Basin in winter, in the belt extending from the Gulf of Lion up to the Levantine Basin and the northern Black Sea. In the other seasons, only few regions experience such high values of τ. The analysis of the wind vorticity shows and quantifies the main cyclonic and anticyclonic circulations, and the study of the joint features of wind stress and vorticity has identified the strongest and most persisting local-scale wind circulations produced by the interaction between the wind flow and the orography. They occur at the lee side of Sardinia–Corse and Crete–Rhodos Islands and persist in all seasons, with some seasonal variation in strength and extent. The areas affected by the orographic disturbances are characterized by high values of wind stress and by a structure of vorticity showing alternating areas of cyclonic and anticyclonic circulations, whose strength is comparable to those of the regional-scale cyclones. [ABSTRACT FROM AUTHOR]

This paper describes an improved edition of the climate division dataset for the conterminous United States (i.e., version 2). The first improvement is to the input data, which now include additional station networks, quality assurance reviews, and temperature bias adjustments. The second improvement is to the suite of climatic elements, which now includes both maximum and minimum temperatures. The third improvement is to the computational approach, which now employs climatologically aided interpolation to address topographic and network variability. Version 2 exhibits substantial differences from version 1 over the period 1895-2012. For example, divisional averages in version 2 tend to be cooler and wetter, particularly in mountainous areas of the western United States. Division-level trends in temperature and precipitation display greater spatial consistency in version 2. National-scale temperature trends in version 2 are comparable to those in the U.S. Historical Climatology Network whereas version 1 exhibits less warming as a result of historical changes in observing practices. Divisional errors in version 2 are likely less than 0.5°C for temperature and 20 mm for precipitation at the start of the record, falling rapidly thereafter. Overall, these results indicate that version 2 can supersede version 1 in both operational climate monitoring and applied climatic research. [ABSTRACT FROM AUTHOR]

This paper describes a time-sensitive approach to climate change projections that was developed as part of New York City's climate change adaptation process and that has provided decision support to stakeholders from 40 agencies, regional planning associations, and private companies. The approach optimizes production of projections given constraints faced by decision makers as they incorporate climate change into long-term planning and policy. New York City stakeholders, who are well versed in risk management, helped to preselect the climate variables most likely to impact urban infrastructure and requested a projection range rather than a single 'most likely' outcome. The climate projections approach is transferable to other regions and is consistent with broader efforts to provide climate services, including impact, vulnerability, and adaptation information. The approach uses 16 GCMs and three emissions scenarios to calculate monthly change factors based on 30-yr average future time slices relative to a 30-yr model baseline. Projecting these model mean changes onto observed station data for New York City yields dramatic changes in the frequency of extreme events such as coastal flooding and dangerous heat events. On the basis of these methods, the current 1-in-10-year coastal flood is projected to occur more than once every 3 years by the end of the century and heat events are projected to approximately triple in frequency. These frequency changes are of sufficient magnitude to merit consideration in long-term adaptation planning, even though the precise changes in extreme-event frequency are highly uncertain. [ABSTRACT FROM AUTHOR]

In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California and compared. Several averaging methodologies are considered and all are found to give similar values when the model grid spacing is less than 3°. This suggests that California is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict California precipitation. This appears to be due mainly to the overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge-satellite observations, which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait that does not seem to be tied to model resolution. The GCM daily and interannual variabilities are generally underpredicted. [ABSTRACT FROM AUTHOR]

This paper studies the selection of optimal crop insurance under climate variability and fluctuating market prices. A model was designed to minimize farmers’ expected losses (including insurance costs) while using the conditional-value-at-risk measure to acquire the risk-aversion level. The application of the model was illustrated by studying a farm with two crops (cotton and peanut) in Jackson County, Florida. The climate variability was caused by ENSO phenomenon. Crop-insurance contracts with minimized losses were 75% actual production history (APH) during El Niño and neutral years and 65% APH during La Niña years for peanut and 75% APH in all ENSO phases for cotton. In addition, risk-averse farmers could select 75% APH for peanut during La Niña years as a means of attaining less expected loss. [ABSTRACT FROM AUTHOR]

Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. The vertical distribution of cloud water is distinctly different among the different models; some common features also appear. Most models underestimate the presence of high clouds, and, although the observed preference for low clouds in the Arctic is present in most of the models, the modeled low clouds are too thin and are displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. In some models this happens also to be where the observations show the highest occurrence of the lowest cloud base; it is not possible to determine if this result is just a coincidence. Different factors contribute to model surface radiation errors. For longwave radiation in summer, a negative bias is present both for cloudy and clear conditions, and intermodel differences are smaller when clouds are present. There is a clear relationship between errors in cloud-base temperature and radiation errors. In winter, in contrast, clear-sky cases are modeled reasonably well, but cloudy cases show a very large intermodel scatter with a significant bias in all models. This bias likely results from a complete failure in all of the models to retain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds. [ABSTRACT FROM AUTHOR]

In this paper, a penalized maximal t test (PMT) is proposed for detecting undocumented mean shifts in climate data series. PMT takes the relative position of each candidate changepoint into account, to diminish the effect of unequal sample sizes on the power of detection. Monte Carlo simulation studies are conducted to evaluate the performance of PMT, in comparison with the most popularly used method, the standard normal homogeneity test (SNHT). An application of the two methods to atmospheric pressure series recorded at a Canadian site is also presented. It is shown that the false-alarm rate of PMT is very close to the specified level of significance and is evenly distributed across all candidate changepoints, whereas that of SNHT can be up to 10 times the specified level for points near the ends of series and much lower for the middle points. In comparison with SNHT, therefore, PMT has higher power for detecting all changepoints that are not too close to the ends of series and lower power for detecting changepoints that are near the ends of series. On average, however, PMT has significantly higher power of detection. The smaller the shift magnitude Δ is relative to the noise standard deviation σ, the greater is the improvement of PMT over SNHT. The improvement in hit rate can be as much as 14%–25% for detecting small shifts (Δ < σ) regardless of time series length and up to 5% for detecting medium shifts (Δ = σ–1.5σ) in time series of length N < 100. For all detectable shift sizes, the largest improvement is always obtained when N < 100, which is of great practical importance, because most annual climate data series are of length N < 100. [ABSTRACT FROM AUTHOR]

In terms of the effects of future climate change upon society, some of the most important parameters to estimate are associated with changing risks of extreme rainfall events, both floods and droughts. However, such aspects of the climate system are hard to estimate well using general circulation models (GCMs)—in particular, for a small mountainous landmass such as New Zealand. This paper describes a downscaling technique using broad-scale changes simulated by GCMs to select past analogs of future climate. The analog samples are assumed to represent an unbiased sample of future rainfall and are used to develop detailed descriptions of rainfall statistics using hidden semi-Markov models of rainfall breakpoint information. Such models are used to simulate long synthetic rainfall time series for comparison with the historical record. Results for three New Zealand sites show overall increases in rainfall with climate change, brought about largely by an increased frequency of rainfall events rather than an increase in rainfall intensity. There was little evidence for significant increases in high-intensity short-duration rainfalls at any site. Such results suggest that, although regional increases of rainfall are consistent with expected future climate changes, it may be that circulation changes, rather than temperature (and vapor pressure) changes, will be the more important determinant of future rainfall distributions, at least for the coming few decades. [ABSTRACT FROM AUTHOR]

The importance of accurately representing the role of clouds in climate change studies has become increasingly apparent in recent years, leading to a substantial increase in the number of satellite sensors and associated algorithms that are devoted to measuring the global distribution of cloud properties. The physics governing the radiative transfer through clouds is well understood, but the impact of uncertainties in algorithm assumptions and the true information content of the measurements in the inverse retrieval problem are generally not as clear, making it difficult to determine the best product to adopt for any particular application. This paper applies information theory to objectively analyze the problem of liquid cloud retrievals from an observing system modeled after the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument currently operating on the Aqua and Terra platforms. It is found that four diagnostics—the retrieval error covariance, the information content, the number of degrees of freedom for signal, and the effective rank of the problem—provide a rigorous test of an observing system. Based on these diagnostics, the combination of the 0.64- and 1.64-μm channels during the daytime and the 3.75- and 11.0-μm channels at night provides the most information for retrieving the properties of the wide variety of liquid clouds modeled. With an eye toward developing a coherent representation of the global distribution of cloud microphysical and radiative properties, these four channels may be integrated into a suitable multichannel inversion methodology such as the optimal estimation or Bayesian techniques to provide a common framework for cloud retrievals under varying conditions. The expected resolution of the observing system for such liquid cloud microphysical property retrievals over a wide variety of liquid cloud is also explored. [ABSTRACT FROM AUTHOR]

The climate of High Mountain Asia (HMA) has changed in recent decades. While the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are inconsistent, with substantial temporal and spatial variation. Climate warming will have enormous consequences for hydroclimatic extremes. For the higher altitudes of the HMA, which are a significant source of water for the large rivers in Asia, often trends are calculated using a limited number of in situ observations mainly observed in valleys. This study explores the changes in mean, extreme, and compound-extreme climate variables and their seasonality along the full altitudinal range in HMA using daily ERA5 reanalysis data (1979–2018). Our results show that winter warming and summer wetting dominate the interior part of HMA. The results indicate a coherent significant increasing trend in the occurrence of heatwaves across all regions in HMA. The number of days with heavy precipitation shows more significant trends in southern and eastern basins than in other areas of HMA. The dry period occurrence shows a distinct demarcation between lower- and higher-altitude regions and is increasing for most basins. Although precipitation and temperature show variable tendencies, their compound occurrence is coherent in the monsoon-dominated basins. These changes in indicators of climatic extremes may imply substantial increases in the future occurrence of hazards such as floods, landslides, and droughts, which in turn impact economic production and infrastructure. [ABSTRACT FROM AUTHOR]

The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters. SIGNIFICANCE STATEMENT: We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns}a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought. [ABSTRACT FROM AUTHOR]

Daylight downward longwave irradiance data recorded over a flat place for the period between April 2001 and December 2004 in Valladolid, Spain, have been compared with estimates generated using four different schemes. The parameterization schemes of Brutsaert, Swinbank, Idso, and Brunt have been considered and calibrated for the comparison. Root-mean-square errors (rmse), mean bias errors, and linear regression correlations have been used to compare measured and estimated values. The results of this comparison show that, for clear-sky conditions, rmse values range between 19.57 and 8.85 W m-2 for calibrated schemes and between 39.78 and 11.13 W m-2 for original ones. The Idso and Brunt schemes give the best results with calibrated coefficients, and the Brunt scheme performs the best with original coefficients. A new scheme for estimating daylight downward longwave irradiance under “all-sky” conditions has been developed based on clear-sky schemes and solar global shortwave irradiance, and, after comparing measured and estimated values by calibrated schemes, it has been found that the Idso and Brunt schemes give the best results. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are challenging to local and regional stakeholders across the United States. The Missouri River basin (MoRB), covering an area over 1.29 million km2, is prone to extreme precipitation events. These events are exacerbated by the complex terrain in the west and the numerous weather and climate features that impact the region on a seasonal and annual basis (low-level jets, mesoscale convective systems, extreme cold air intrusions, etc.). Without an in-depth analysis of extreme precipitation in the MoRB, the evolving nature of extreme precipitation is not known. This situation warrants an analysis of extreme precipitation, especially relating to subannual variations when extreme precipitation is more impactful. To this end, data from 131 U.S. Historical Climatology Network (USHCN) stations were used to determine the nature of extreme precipitation from 1950 to 2019. Annual 99th-percentile events and annual station maximum precipitation events occur more frequently in the eastern MoRB than in the western MoRB, in line with the annual precipitation climatology. Results show that 99th-percentile events and annual station maximum precipitation events are becoming more frequent across the MoRB. Through analysis of 3-month extreme precipitation trends, areas in the eastern and southern MoRB are shown to have an increase in event frequency and intensity. Frequency shifts in the 99th-percentile events, however, have occurred across the entire region. The increasing frequency of extreme events in the western MoRB represents a significant change for the hydroclimate of the region. Overall, the results from this work show that MORB extreme precipitation has increased in frequency and intensity during the 1950–2019 period. [ABSTRACT FROM AUTHOR]

A quantile-based bias-correction method is applied to a seven-member dynamic ensemble of global wave climate simulations with the aim of reducing the significant wave height HS, mean wave period Tm, and mean wave direction (MWD) biases, in comparison with theERA5 reanalysis. The corresponding projected changes toward the end of the twenty-first century are assessed. SevenCMIP5EC-EARTHruns (single forcing) were used to force sevenwavemodel(WAM)realizations (singlemodel), following theRCP8.5 scenario (single scenario). The biases for the 1979-2005 reference period (present climate) are corrected using the empirical Gumbel quantile mapping and empirical quantile mapping methods. The same bias-correction parameters are applied to the HS, Tm (and wave energy fluxPw), andMWDfuture climate projections for the 2081-2100 period. The bias-corrected projected changes show increases in the annualmeanHS (14%), Tm (6.5%), and Pw (30%) in the Southern Hemisphere and decreases in the Northern Hemisphere (mainly in the North Atlantic Ocean) that are more pronounced during local winter. For the upper quantiles, the bias-corrected projected changes aremore striking during local summer, up to 120%, for Pw. After bias correction, the magnitude of the HS, Tm, and Pw original projected changes has generally increased. These results, albeit consistent with recent studies, show the relevance of a quantile-based bias-correction method in the estimation of the future projected changes in swave climate that is able to deal with the misrepresentation of extreme phenomena, especially along the tropical and subtropical latitudes. [ABSTRACT FROM AUTHOR]

Climate normals are traditionally calculated every decade as the average values over a period of time, often 30 years. Such an approach assumes a stationary climate, with several alternatives recently introduced to account for monotonic climate change. However, these methods fail to account for interannual climate variability [e.g., El Niño–Southern Oscillation (ENSO)] that systematically alters the background state of the climate similar to climate change. These effects and their uncertainties are well established, but they are not reflected in any readily available climate normals datasets. A new high-resolution set of normals is derived for the contiguous United States that accounts for ENSO and uses the optimal climate normal (OCN)—a 10-yr (15 yr) running average for temperature (precipitation)—to account for climate change. Anomalies are calculated by subtracting the running means and then compositing into 5 ENSO phase and intensity categories: Strong La Niña, Weak La Niña, Neutral, Weak El Niño, and Strong El Niño. Seasonal composites are produced for each of the five phases. The ENSO normals are the sum of these composites with the OCN for a given month. The result is five sets of normals, one for each phase, which users may consult with respect to anticipated ENSO outcomes. While well-established ENSO patterns are found in most cases, a distinct east–west temperature anomaly pattern emerges for Weak El Niño events. This new product can assist stakeholders in planning for a broad array of possible ENSO impacts in a changing climate. [ABSTRACT FROM AUTHOR]

The CMIP5 decadal hindcast ("Hindcast") and prediction ("Predict") experiment simulations from 11 models were analyzed for the United States with respect to two metrics of extreme precipitation: the 10-yr return level of daily precipitation, derived from the annual maximum series of daily precipitation, and the total precipitation exceeding the 99.5th percentile of daily precipitation. Both Hindcast simulations and observations generally show increases for the 1981–2010 historical period. The multimodel-mean Hindcast trends are statistically significant for all regions while the observed trends are statistically significant for the Northeast, Southeast, and Midwest regions. An analysis of CMIP5 simulations driven by historical natural ("HistoricalNat") forcings shows that the Hindcast trends are generally within the 5th–95th-percentile range of HistoricalNat trends, but those outside that range are heavily skewed toward exceedances of the 95th-percentile threshold. Future projections for 2006–35 indicate increases in all regions with respect to 1981–2010. While there is good qualitative agreement between the observations and Hindcast simulations regarding the direction of recent trends, the multimodel-mean trends are similar for all regions, while there is considerable regional variability in observed trends. Furthermore, the HistoricalNat simulations suggest that observed historical trends are a combination of natural variability and anthropogenic forcing. Thus, the influence of anthropogenic forcing on the magnitude of near-term future changes could be temporarily masked by natural variability. However, continued observed increases in extreme precipitation in the first decade (2006–15) of the "future" period partially confirm the Predict results, suggesting that incorporation of increases in planning would appear prudent. [ABSTRACT FROM AUTHOR]

This study evaluates how well reanalyses represent daily and multiday accumulated precipitation (hereinafter daily PCP) over British Columbia, Canada (Part I evaluated 2-m temperature). Reanalyses are compared with observations from 66 meteorological stations distributed over the complex terrain of British Columbia, separated into climate regions by k-means clustering. Systematic error, two-sample χ2 statistic, and frequency of daily PCP occurrence are evaluated from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), the Climate Forecast System Reanalysis (CFSR), the Japanese 55-year Reanalysis (JRA-55), and the latest Modern-Era Retrospective Analysis for Research and Applications (version 2; MERRA-2). The 2- and 30-yr return levels of daily PCP are estimated from a generalized extreme value (GEV) distribution fitted by the method of L moments, and their systematic errors are analyzed. JRA-55 and MERRA-2 generally outperform ERA-Interim and CFSR across all metrics. Biases are largely explained by poor reanalysis representation of terrain characteristics such as steepness, exposure, elevation, location of barriers, and wind speed and direction. Statistical stationarity of precipitation intensity and frequency over the 30-yr period is assessed by using confidence intervals and GEV distributions fitted with and without time-dependent parameters. It is determined that stationary distributions are sufficient to represent the climate of daily PCP for this region and time period. [ABSTRACT FROM AUTHOR]

A suite of modern atmospheric reanalyses is analyzed to determine how they represent North American supercell environments. This analysis is performed by comparing a database of Rapid Update Cycle (RUC-2) proximity soundings with profiles derived from the nearest grid point in each reanalysis. Parameters are calculated using the Sounding and Hodograph Analysis and Research Program in Python (SHARPpy), an open-source Python sounding-analysis package. Representation of supercell environments varies across the reanalyses, and the results have ramifications for climatological studies that use these datasets. In particular, thermodynamic parameters such as the convective available potential energy (CAPE) show the widest range in biases, with reanalyses falling into two camps. The North American Regional Reanalysis (NARR) and the Japanese 55-year Reanalysis (JRA-55) are similar to RUC-2, but other reanalyses have a substantial negative bias. The reasons for these biases vary and range from thermodynamic biases at the surface to evidence of convective contamination. Overall, it is found that thermodynamic biases feed back to other convective parameters that incorporate CAPE directly or indirectly via the effective layer. As a result, significant negative biases are found for indices such as the supercell composite parameter. These biases are smallest for NARR and JRA-55. Kinematic parameters are more consistent across the reanalyses. Given the issues with thermodynamic properties, better segregation of soundings by storm type is found for fixed-layer parameters than for effective-layer shear parameters. Although no reanalysis can exactly reproduce the results of earlier RUC-2 studies, many of the reanalyses can broadly distinguish between environments that are significantly tornadic versus nontornadic. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia's wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate-viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981-2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030-59) minus present (1970-99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia's wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate. [ABSTRACT FROM AUTHOR]

Monitoring drought conditions in arid and semiarid regions characterized by high levels of intra- and interannual hydroclimatic variability is a challenging task. Typical drought-monitoring indices that are based on monthly-scale data lack sufficient temporal resolution to detect hydroclimatic extremes and, when used operationally, may not provide adequate indication of drought status. In a case study focused on the Four Corners region of the southwestern United States, the authors used recently standardized World Meteorological Organization climate extremes indices to discern intra-annual hydroclimatic extremes and diagnose potential drought status in conjunction with the simple metric of annual total precipitation. By applying datareduction methods to a suite of metrics calculated using daily data for 1950-2014, the authors identified five extremes indices that provided additional insight into interannual hydroclimatic variability. Annual time series of these indices revealed anomalous years characterized by shifts in the seasonal distribution of precipitation and in the intensity and frequency of individual events. The driest 4-yr intervals over the study period, characterized by similar annual and interval total precipitation anomalies, represent dramatically different assemblages of index values, which are interpreted as different "flavors" of drought. In turn, it is expected that varying drought impacts on ecosystems, agricultural systems, and water resources would emerge under these different flavors of drought. Results from this study indicate that operational drought monitoring and historical drought assessments in arid and semiarid regions would benefit from the additional insight that daily-based hydroclimatic extremes indices provide, especially in light of expected climate change-driven changes to the hydrologic cycle. [ABSTRACT FROM AUTHOR]

Climate change will inevitably continue for the next few decades and will have an impact on climate-sensitive agricultural production, emphasizing the need to design effective adaptive strategies to cope with climate risk or take advantage of potential climatic benefits. In this study, the latest version of the Crop Environment Resource Synthesis-Rice (CERES-Rice) model was applied to assess the impacts of climate change and carbon dioxide (CO2) fertilization on rice yield, as well as the effectiveness of two popularly adopted adaptive measures in Hunan Province, the main rice-production location in China. The simulation spanned 30 years of baseline (1981-2010) as well as three future periods (2011-40, 2041-70, and 2071-99) with climate data generated by five general circulation models under the newly developed representative concentration pathway (RCP) 4.5 and 8.5 scenarios. The simulation results showed that, in comparison with average rice yield during the baseline (1981-2010), the ensemble-average yield of all cultivars during the 2020s, 2050s, and 2080s would decrease under both RCPs without CO2 fertilization effects. The ensemble-average yield reduction during the 2080s was alleviated under both RCPs if CO2 fertilization effects were accounted for. Adaptation simulations indicated that two adaptive measures (switching cultivars and changing planting dates) could mitigate the adverse effect to different extents. The intermodel variability under both RCPs was generally small. These findings may provide useful insight into the potential impacts of climate change on rice yield and effective adaptive measures to mitigate the adverse effect of future climate change in Hunan Province. [ABSTRACT FROM AUTHOR]

The characteristics and conditions favoring extreme hurricanes remain largely unknown because of their small number in the observational record. Synthetic tracks are capable of providing a large, representative sample of these events, which provides an opportunity to further understanding of extreme characteristics as compared with those of more common tropical cyclones. The authors compare 300 synthetic extreme (100-yr event, ≥48.9 m s−1) and 300 common (5-yr event, ≤33.6 m s−1) tropical cyclones for Charleston, South Carolina, for differences in spatial, temporal, and other characteristics. Results suggest that extreme hurricanes have a more-defined spatial and temporal behavior, generally forming off the coast of Africa and making a direct landfall at Charleston. Common tropical cyclones sometimes make prior landfalls, may approach from either the Gulf of Mexico or the Atlantic Ocean, and often decay well before reaching Charleston. They are likely to occur through much of the hurricane season, whereas extreme events are most common during a short period toward the end of August. There is no significant difference between common and extreme translational velocity at landfall. This study demonstrates the opportunity that synthetic tracks provide for understanding the rarest hurricanes and provides initial insight into those affecting Charleston. [ABSTRACT FROM AUTHOR]

The European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) has been downscaled using a regional model covering Alaska at 20-km spatial and hourly temporal resolution for 1979-2013. Stakeholders can utilize these enhanced-resolution data to investigate climate- and weather-related phenomena in Alaska. Temperature and precipitation are analyzed and compared among ERA-Interim, WRF Model downscaling, and in situ observations. Relative to ERA-Interim, the downscaling is shown to improve the spatial representation of temperature and precipitation around Alaska's complex terrain. Improvements include increased winter and decreased summer higher-elevation downscaled seasonal average temperatures. Precipitation is also enhanced over higher elevations in all seasons relative to the reanalysis. These spatial distributions of temperature and precipitation are consistent with the few available gridded observational datasets that account for topography. The downscaled precipitation generally exceeds observationally derived estimates in all seasons over mainland Alaska, and it is less than observations in the southeast. Temperature biases tended to be more mixed, and the downscaling reduces absolute bias at higher elevations, especially in winter. Careful selection of data for local site analysis from the downscaling can help to reduce these biases, especially those due to inconsistencies in elevation. Improved meteorological station coverage at higher elevations will be necessary to better evaluate gridded downscaled products in Alaska because biases vary and may even change sign with elevation. [ABSTRACT FROM AUTHOR]

The air-freezing index (AFI) is a common metric for determining the freezing severity of the winter season and estimating frost depth for midlatitude regions, which is useful for determining the depth of shallow foundation construction. AFI values represent the seasonal magnitude and duration of below-freezing air temperature. Departures of the daily mean temperature above or below 0°C (32°F) are accumulated over each August-July cold season; the seasonal AFI value is defined as the difference between the highest and lowest extrema points. Return periods are computed using generalized extreme value distribution analysis. This research replaces the methodology used by the National Oceanic and Atmospheric Administration to calculate AFI return periods for the 1951-80 time period, applying the new methodology to the 1981-2010 climate normals period. Seasonal AFI values and return period values were calculated for 5600 stations across the coterminous United States (CONUS), and the results were validated using U.S. Climate Reference Network temperature data. Return period values are typically 14%-18% lower across CONUS during 1981-2010 versus a recomputation of 1951-80 return periods with the new methodology. For the 100-yr (2 yr) return periods, about 59% (83%) of stations show a decrease of more than 10% in the more recent period, whereas 21% (2%) show an increase of more than 10%, indicating a net reduction in winter severity that is consistent with observed climate change. [ABSTRACT FROM AUTHOR]

Recent research suggests that high levels of ambient near-surface atmospheric turbulence are often associated with rapid and sometimes erratic wildland fire spread that may eventually lead to large burn areas. Previous research has also examined the feasibility of using near-surface atmospheric turbulent kinetic energy (TKE s) alone or in combination with the Haines index (HI) as an additional indicator of anomalous atmospheric conditions conducive to erratic or extreme fire behavior. However, the application of TKE s-based indices for operational fire-weather predictions in the United States on a regional or national basis first requires a climatic assessment of the spatial and temporal patterns of the indices that can then be used for testing their operational effectiveness. This study provides an initial examination of some of the spatial and temporal variability patterns across the United States of TKE s and the product of HI and TKE s (HITKE s) using data from the North American Regional Reanalysis dataset covering the 1979-2008 period. The analyses suggest that there are regional differences in the behavior of these indices and that regionally dependent threshold values for TKE s and HITKE s may be needed for their potential use as operational indicators of anomalous atmospheric turbulence conditions conducive to erratic fire behavior. The analyses also indicate that broad areas within the northeastern, southeastern, and southwestern regions of the United States have experienced statistically significant positive trends in TKE s and HITKE s values over the 1979-2008 period, with the most substantial increases in values occurring over the 1994-2008 period. [ABSTRACT FROM AUTHOR]

A coupled biosphere-atmosphere statistical-dynamical model is used to study the relative roles of the impact of the land change caused by tropical deforestation and global warming on energy balance and climate. Three experiments were made: 1) deforestation, 2) deforestation + 2 × CO2, and 3) deforestation + CO2, CH4, N2O, and O3 for 2100. In experiment 1, the climatic impact of the Amazonian deforestation is studied. In experiment 2, the effect of doubling CO2 is included. In experiment 3, the concentrations of the greenhouse gases (GHGs) correspond to the A1FI scenario from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. The results showed that the percentage of the warming caused by deforestation relative to the warming when the increase in GHG concentrations is included is higher than 60% in the tropical region. On the other hand, with the increase in GHG concentrations, a reduction in the decrease of evapotranspiration and precipitation in the tropical region occurs when compared with the deforestation case. Because of an increase in the net longwave flux at the surface, there is a reduction in the decrease of the surface net radiation flux when compared with the case of only deforestation. This leads to an increase in the surface temperature. Although the changes are higher at 5°S, the percentage of them when the increase in GHG concentrations is included together with deforestation relative to the case of only deforestation is higher at 5°N (higher than 50% for the surface temperature and higher than 90% for the foliage and air foliage temperatures) in both experiments 2 and 3. [ABSTRACT FROM AUTHOR]

The impact of climate change on rice productivity in China remains highly uncertain because of uncertainties from climate change scenarios, parameterizations of biophysical processes, and extreme temperature stress in crop models. Here, the Model to Capture the Crop-Weather Relationship over a Large Area (MCWLA)-Rice crop model was developed by parameterizing the process-based general crop model MCWLA for rice crop. Bayesian probability inversion and a Markov chain Monte Carlo technique were then applied to MCWLA-Rice to analyze uncertainties in parameter estimations and to optimize parameters. Ensemble hindcasts showed that MCWLA-Rice could capture the interannual variability of the detrended historical yield series fairly well, especially over a large area. A superensemble-based probabilistic projection system (SuperEPPS) coupled to MCWLA-Rice was developed and applied to project the probabilistic changes of rice productivity and water use in eastern China under scenarios of future climate change. Results showed that across most cells in the study region, relative to 1961-90 levels, the rice yield would change on average by 7.5%-17.5% (from −10.4% to 3.0%), 0.0%-25.0% (from −26.7% to 2.1%), and from −10.0% to 25.0% (from −39.2% to −6.4%) during the 2020s, 2050s, and 2080s, respectively, in response to climate change, with (without) consideration of CO2 fertilization effects. The rice photosynthesis rate, biomass, and yield would increase as a result of increases in mean temperature, solar radiation, and CO2 concentration, although the rice development rate could accelerate particularly after the heading stage. Meanwhile, the risk of high-temperature stress on rice productivity would also increase notably with climate change. The effects of extreme temperature stress on rice productivity were explicitly parameterized and addressed in the study. [ABSTRACT FROM AUTHOR]

Climate-related disasters in Bolivia are frequent, severe, and manifold and affect large parts of the population, economy, and ecosystems. Potentially amplified through climate change, natural hazards are of growing concern. To better understand these events, homogenized daily observations of temperature (29 stations) and precipitation (68 stations) from 1960 to 2009 were analyzed in this study. The impact of the positive (+) and negative (−) phases of the three climate modes (i) Pacific decadal oscillation (PDO), (ii) El Niño-Southern Oscillation (ENSO) with El Niño (EN) and La Niña (LN) events, and (iii) Antarctic Oscillation (AAO) were assessed. Temperatures were found to be higher during PDO(+), EN, and AAO(+) in the Andes. Total amounts of rainfall, as well as the number of extreme events, were higher during PDO(+), EN, and LN in the lowlands. During austral summer [December-February (DJF)], EN led to drier conditions in the Andes with more variable precipitation. Temperatures increased at a rate of 0.1°C per decade, with stronger increases in the Andes and in the dry season. Rainfall totals increased from 1965 to 1984 [12% in DJF and 18% in June-August (JJA)] and decreased afterward (−4% in DJF and −10% in JJA), following roughly the pattern of PDO. Trends of climate extremes generally corresponded to trends of climate means. Findings suggest that Bolivia's climate will be warmer and drier than average in the near-term future. Having entered PDO(−) in 2007, droughts and LN-related floods can be expected in the lowlands, while increasing temperatures suggest higher risks of drought in the Andes. [ABSTRACT FROM AUTHOR]

A method to estimate objectively the surface wind fields associated with tropical cyclones using only data from multiple satellite platforms and satellite-based wind retrieval techniques is described. The analyses are computed on a polar grid using a variational data-fitting method that allows for the application of variable data weights to input data. The combination of gross quality control and the weighted variational analysis also produces wind estimates that have generally smaller errors than do the raw input data. The resulting surface winds compare well to the NOAA Hurricane Research Division H**Wind aircraft reconnaissance-based surface wind analyses, and operationally important wind radii estimated from these wind fields are shown to be generally more accurate than those based on climatological data. Most important, the analysis system produces global tropical cyclone surface wind analyses and related products every 6 h-without aircraft reconnaissance data. Also, the analysis and products are available in time for consideration by forecasters at the Joint Typhoon Warning Center, the Central Pacific Hurricane Center, and the National Hurricane Center in preparing their forecasts and advisories. This Multiplatform Tropical Cyclone Surface Wind Analysis (MTCSWA) product is slated to become an operationally supported product at the National Environmental Satellite Data and Information Service (NESDIS). The input data, analysis method, products, and verification statistics associated with the MTCSWA are discussed within. [ABSTRACT FROM AUTHOR]

The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model's performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed. [ABSTRACT FROM AUTHOR]

A pragmatic approach to estimate the impact of climate change on the urban environment, here called the cuboid method, is presented. This method allows one to simulate the urban heat load and the frequency of air temperature threshold exceedances using only eight microscale urban climate simulations for each relevant wind direction and time series of daily meteorological parameters either from observations or regional climate projections. Eight representative simulations are designed to encompass all major potential urban heat-stress conditions. From these representative simulations, the urban-heat-load conditions in any weather situation are derived by interpolation. The presented approach is applied to study possible future heat load in Frankfurt, Germany, using the high-resolution Microscale Urban Climate Model in three dimensions (MUKLIMO_3). To estimate future changes in heat-load-related climate indices in Frankfurt, climate projections from the regional climate models Max Planck Institute Regional Model (REMO), Climate Limited-Area Model (CLM), Wetterlagen-basierte Regionalisierungsmethode (WETTREG), and Statistical Regional Model (STAR) are used. These regional climate models are driven by the ''ECHAM5'' general circulation model and Intergovernmental Panel on Climate Change emission scenario A1B. For the mean annual number of days with a maximum daily temperature exceeding 25°°C, a comparison between the cuboid method results from observed and projected regional climate time series of the period 1971--2000 shows good agreement, except for CLM for which a clear underestimation is found. On the basis of the 90%% significance level of all four regional climate models, the mean annual number of days with a maximum daily temperature exceeding 25°°C in Frankfurt is expected to increase by 5--32 days for 2021--50 as compared with 1971--2000. [ABSTRACT FROM AUTHOR]

Numerical simulation techniques and idealized reservoir management models are used to assess the utility of climate information for the effective management of a single multiobjective reservoir. Reservoir management considers meeting release and reservoir volume targets and minimizing wasteful spillage. The influence of reservoir size and inflow variability parameters on the management benefits is examined. The effects of climate and demand (release target) change on the management policies and performance are also quantified for various change scenarios. Inflow forecasts emulate ensembles of dynamical forecasts for a hypothetical climate system with somewhat predictable low-frequency variability. The analysis considers the impacts of forecast skill. The mathematical problem is cast in a dimensionless time and volume framework to allow generalization. The present work complements existing research results for specific applications and expands earlier analytical results for simpler management situations in an effort to draw general conclusions for the present-day reservoir management problem under uncertainty. The findings support the following conclusions: (i) reliable inflow forecasts are beneficial for reservoir management under most situations if adaptive management is employed; (ii) tolerance to forecasts of lower reliability tends to be higher for larger reservoirs; (iii) reliable inflow forecasts are most useful for a midrange of reservoir capacities; (iv) demand changes are more detrimental to reservoir management performance than inflow change effects of similar magnitude; (v) adaptive management is effective for mitigating climatic change effects and may even help to mitigate demand change effects. [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]

The algorithm described in Part I has been applied to the millimeter cloud radar observations from January 1999 to December 2005 at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (including Manus and Nauru) sites. Approximately 10 000 cirrus hours from each of these sites were analyzed. Retrieved cloud properties including condensed mass, particle size, optical depth, and in-cloud vertical air motions were analyzed in terms of their geographical, seasonal, and diurnal variations. The analysis shows that tropical ice clouds observed by millimeter radar are very different from ice clouds at SGP, with the tropical clouds having slightly larger particle sizes and greater ice masses and being more likely to be associated with ascending air motions, in addition to being colder and higher in altitude. A positive residual of derived in-cloud air motion found in the tropical data likely provides evidence for lofting of air into the tropopause transition layer as a result of radiative heating. The midlatitude cirrus demonstrate strong seasonal variations with more frequent, thicker clouds occurring during the summer than during the winter. Very subtle seasonal variations are found for tropical ice clouds, and evidence is presented that cirrus properties vary interannually and are correlated with El Niño oscillations. In addition, it is found that tropical cirrus demonstrate a stronger diurnal cycle than cirrus of the midlatitudes, with the in-cloud updrafts peaking in the early afternoon. [ABSTRACT FROM AUTHOR]

Numerical and observational analyses were conducted using realistic and historical three-set land-use data over 40 yr from 1963 to 2002 to evaluate regional warming in the Daegu metropolitan area due to dramatic land-use alterations in the basin area and to quantitatively estimate the influence of nonuniform regional warming on complex local circulation. The results are as follows: (a) The daily mean temperature in the Daegu metropolitan area increased by 1.5 K over 40 yr, and the increase was higher than that of the mean temperature on the Korean Peninsula. (b) A simulated surface wind pattern in 2002 agreed well with the observed data. The rapid urbanization of the Daegu metropolitan area has had a large influence on the local circulation outside of the area. (c) Because of the variation in heat and momentum transfer due to land-use alteration, the prevailing wind has also changed in the central basin in the Daegu metropolitan area. The spatial distribution of the temperature change is very similar to the changes in the wind. (d) The two-dimensional mixed-height theory was applied to local circulations. By this theory, regional warming that occurs as a result of land-use alteration determines a higher critical height, which serves as an index for estimating the mixing intensity induced by a surface sensible heat flux. According to the observational data, this index can be used to quantitatively estimate regional warming in complex terrain. [ABSTRACT FROM AUTHOR]

Solar radiation is one of the most important factors affecting climate and the environment. Routine measurements of irradiance are valuable for climate change research because of long time series and areal coverage. In this study, a set of quality assessment (QA) algorithms is used to test the quality of daily solar global, direct, and diffuse radiation measurements taken at 122 observatories in China during 1957–2000. The QA algorithms include a physical threshold test (QA1), a global radiation sunshine duration test (QA2), and a standard deviation test applied to time series of annually averaged solar global radiation (QA3). The results show that the percentages of global, direct, and diffuse solar radiation data that fail to pass QA1 are 3.07%, 0.01%, and 2.52%, respectively; the percentages of global solar radiation data that fail to pass the QA2 and QA3 are 0.77% and 0.49%, respectively. The method implemented by the Global Energy Balance Archive is also applied to check the data quality of solar radiation in China. Of the 84 stations with a time series longer that 20 yr, suspect data at 35 of the sites were found. Based on data that passed the QA tests, trends in ground solar radiation and the effect of the data quality assessment on the trends are analyzed. There is a decrease in ground solar global and direct radiation in China over the years under study. Although the quality assessment process has significant effects on the data from individual stations and/or time periods, it does not affect the long-term trends in the data. [ABSTRACT FROM AUTHOR]

This review article enumerates, categorizes, and compares many of the methods that have been proposed to detect undocumented changepoints in climate data series. The methods examined include the standard normal homogeneity (SNH) test, Wilcoxon’s nonparametric test, two-phase regression (TPR) procedures, inhomogeneity tests, information criteria procedures, and various variants thereof. All of these methods have been proposed in the climate literature to detect undocumented changepoints, but heretofore there has been little formal comparison of the techniques on either real or simulated climate series. This study seeks to unify the topic, showing clearly the fundamental differences among the assumptions made by each procedure and providing guidelines for which procedures work best in different situations. It is shown that the common trend TPR and Sawa’s Bayes criteria procedures seem optimal for most climate time series, whereas the SNH procedure and its nonparametric variant are probably best when trend and periodic effects can be diminished by using homogeneous reference series. Two applications to annual mean temperature series are given. Directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Mean monthly wind speed at 70 m above ground level is investigated for 11 sites in Minnesota for the period 1995–2003. Wind speeds at these sites show significant spatial and temporal coherence, with prolonged periods of above- and below-normal values that can persist for as long as 12 months. Monthly variation in wind speed primarily is determined by the north–south pressure gradient, which captures between 22% and 47% of the variability (depending on the site). Regression on wind speed residuals (pressure gradient effects removed) shows that an additional 6%–15% of the variation can be related to the Arctic Oscillation (AO) and Niño-3.4 sea surface temperature (SST) anomalies. Wind speeds showed little correspondence with variation in the Pacific–North American (PNA) circulation index. The effect of the strong El Niño of 1997/98 on the wind speed time series was investigated by recomputing the regression equations with this period excluded. The north–south pressure gradient remains the primary determinant of mean monthly 70-m wind speeds, but with 1997/98 removed the influence of the AO increases at nearly all stations while the importance of the Niño-3.4 SSTs generally decreases. Relationships with the PNA remain small. These results suggest that long-term patterns of low-frequency wind speed (and thus wind power) variability can be estimated using large-scale circulation features as represented by large-scale climatic datasets and by climate-change models. [ABSTRACT FROM AUTHOR]

Understanding of climate influence on crop yields can help in the design of policies to reduce climate-related vulnerability in many parts of the world, including the target of this case study—the state of Ceará, Brazil. The study has examined the relationships between climate variations and corn yields and, in addition, has estimated the potential predictability of corn yields in Ceará drawing on the now well-established seasonal predictability of the region’s climate based on prevailing patterns of sea surface temperature (SST), especially in the tropical Atlantic and tropical Pacific Oceans. The relationships between corn yields and climate variables have been explored using observed data for the period of 1952–2001. A linear regression–based corn-yield model was evaluated by comparing the model-simulated yields with the observations using three goodness-of-fit measures: the coefficient of determination, the index of agreement, and the mean absolute error. A comparative performance analysis was carried out on several climate variables to determine the most appropriate climate index for simulating corn yields in Ceará. A weather index was defined to measure the severity of drought and flooding conditions in the growing season for corn. The analysis indicated that the weather index is the best climate parameter for simulating corn yields in Ceará. The observed weather index can explain 56.8% of the variance of the observed corn yields. High potential predictability of the weather index was revealed by the evaluation of an ensemble of 10 runs with the NCEP Regional Spectral Model nested into the ECHAM4.5 atmospheric general circulation model, driven with observed SSTs in each season for the period of 1971–2000. Whereas these runs are based on the actual observed SST pattern in each season, other studies have shown that persistence of SST over several months is sufficient for a true predictive capability. The aim here was to show that the SST-forced component of climate variation does translate into the weather features that are important for crop yields. Indeed, the results demonstrate the striking extent to which the year-to-year changes in SST force local climate characteristics that can specify the year-to-year variations in corn yields. The variance of corn yield explained by the SST-driven model was 49.5%. [ABSTRACT FROM AUTHOR]

The winter climate of the British Isles is characterized by considerable interannual variability, which, because of the general climate sensitivity of a number of health outcomes, places at times considerable pressure on the provision of health services. Seasonal climate forecasts potentially could improve management within the health sector and assist in hedging against the vagaries of climatic variability. For this reason, an exploratory analysis of the potential utility of seasonal climate forecasting for the health sector in the United Kingdom is presented here. Study results revealed that the general level of winter mortality at the monthly to seasonal time scale possesses a strong association with simple descriptors of winter climate such as maximum temperature and the number of days below a given temperature threshold. Because such climate indices can be derived from the output of coupled seasonal climate prediction models, predictions of general levels of mortality may be possible using simple transfer functions that describe winter climate and health associations. Despite the potential one-month-ahead and one-season-ahead predictability of winter mortality levels, the predictability of the key climate indices by coupled climate models is shown to be somewhat limited, which compromises the ability to predict general levels of winter mortality for all months except February. [ABSTRACT FROM AUTHOR]

Analyses of trends of the orographic winter precipitation enhancement factor Ro along the coastal mountain ranges of the west coast of the United States show a pattern of decreasing Ro during the last century by as much as -24% from the southern border to central California, to no decrease in northern California and Oregon, and to a renewed decrease of Ro (-14%) in Washington to the Seattle area east of the Puget Sound. Similar decreases also occurred well inland, over Arizona, New Mexico, Utah (this study), and the east slopes of the Colorado Rockies (already documented prior to this study). Both absolute precipitation amounts and Ro are affected by fluctuations in the atmospheric circulation patterns, such as those associated with the Pacific decadal oscillation and the Southern Oscillation index. However, these climatic fluctuations cannot explain the observed trends in Ro. Although the trends of aerosols are available only since 1988, aerosol measurements from the Interagency Monitoring of Protected Visual Environments (IMPROVE) aerosol monitoring network show that negative trends in Ro are associated with elevated concentrations of fine aerosols [particulate matter less than 2.5 μm in diameter (PM 2.5)]. The PM 2.5 showed stability or some increase in the areas where their levels were elevated and decreasing trends of Ro were noted. Strong decreasing trends of the coarse aerosols (PM 10–PM 2.5) were noted, especially in the areas with elevated levels of PM 2.5. The decreasing trend in coarse aerosols (the coarse aerosols may act to initiate and enhance precipitation), in conjunction with the constancy and/or increases of the small aerosols (small aerosols suppress precipitation), can explain the continuing decreases in orographic precipitation during the last two decades despite the indicated improvement in the conventional air quality standards. [ABSTRACT FROM AUTHOR]