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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]

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 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]

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]

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]