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The time of emergence (TOE) of climate change is defined as the time when a new climate state emerges from a prior one. TOE assessment is particularly relevant in West Africa, a region highly threatened by climate change and urgently needing trustworthy climate predictions. In this paper, the TOE of precipitation change in West Africa is assessed for the first time, by analyzing 6 precipitation metrics (cumulated precipitation, number of wet and very wet days, onset and length of the rainy season) computed from the output of 29 state-of-the-art climate models. In West Sahel, climate conditions characterized by reduced occurrence of wet days are likely to emerge before 2036, leading to the possible emergence of a dryer climate in 2028–2052. In East Sahel, a wetter precipitation regime characterized by increased occurrence of very wet days is likely to emerge before 2054. Results do not provide a clear indication about a possible climate shift in the onset and length of the rainy season. Although uncertainty in climate model future projections still limits the robust determination of TOE locally, this study provides reliable time constraints to the expected climate shift in West Africa at the sub-regional scale, supporting adaptation measures to the future change in the precipitation regime. [ABSTRACT FROM AUTHOR]

Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research associated with this warming. The rapid warming facilitates intense and broad glacier melt over most of the TP, although some glaciers in the northwest are advancing. By heating the atmosphere and reducing snow/ice albedo, aerosols also contribute to the glaciers melting. Glacier melt is accompanied by lake expansion and intensification of the water cycle over the TP. Precipitation has increased over the eastern and northwestern TP. Meanwhile, the TP is greening and most regions are experiencing advancing phenological trends, although over the southwest there is a spring phenological delay mainly in response to the recent decline in spring precipitation. Atmospheric and terrestrial thermal and dynamical processes over the TP affect the Asian monsoon at different scales. Recent evidence indicates substantial roles that mesoscale convective systems play in the TP's precipitation as well as an association between soil moisture anomalies in the TP and the Indian monsoon. Moreover, an increase in geohazard events has been associated with recent environmental changes, some of which have had catastrophic consequences caused by glacial lake outbursts and landslides. Active debris flows are growing in both frequency of occurrences and spatial scale. Meanwhile, new types of disasters, such as the twin ice avalanches in Ali in 2016, are now appearing in the region. Adaptation and mitigation measures should be taken to help societies' preparation for future environmental challenges. Some key issues for future TP studies are also discussed. [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]

In recent years, numerous studies have investigated the relationship of extreme precipitation intensities with temperature derived from a binning method using present‐day weather data. The aim of this approach is that these dependencies, or so‐called apparent scaling rates, provide insights on how precipitation extremes could evolve in the future climate. In a recent paper, Bao et al. showed that there is a large discrepancy between the present‐day apparent scaling rates—showing a negative dependency of daily rainfall extremes on temperature for Darwin—and the climate change response showing a substantial increase in daily rainfall extremes. Their results provide compelling evidence that it may not be straightforward to predict the climate change response from the temperature scaling. We agree with this statement, but disagree with the physical explanation put forward in that study and think that moisture limitations are the most likely cause. Therefore, we advocate the use of dew point temperatures in scaling studies as a much more direct proxy of the absolute humidity of the air in which precipitation extremes develop, which we think is physically more consistent with the underlying mechanism causing the increase in precipitation extremes with global warming. [ABSTRACT FROM AUTHOR]

In this paper we analyse the relationship between the mean and extreme precipitation for five stations in Hungary and the circulation types at the 500 hPa level, for a 52‐year period, 1958–2010. We used daily rainfall data for each station and a calendar of the daily circulation types at the 500 hPa geopotential height for Hungary based on an automatic classification scheme. A trend analysis showed that, in general, the mean and extreme precipitation rarely exhibit significant trends, similarly to previous studies. The only exception was found for the number of annual and summer rainy days in Budapest with significant negative trends (p = .05), as well as the number of days with rainfall amounts greater than the 95% probability level for Debrecen with a significant positive trend. It was found that the circulation types that are mainly responsible for the largest precipitation amounts overall show (in some cases significant) negative trends, as well as the precipitation amounts that are associated with them (Cwsw, Cse, Cwnw). In most cases, the circulation types that are prevailing during the extreme precipitation events are associated with western or almost‐western atmospheric circulations (Cwsw, Ase) and Mediterranean‐ or Atlantic‐origin depressions. [ABSTRACT FROM AUTHOR]

This paper analyses the daily precipitation data recorded for the last 75-80 years in SE Spain. Prior to statistical analysis, homogeneity tests were performed to determine the usefulness of the data (recorded in six pluviometric stations). If the data were homogeneous, the Mann–Kendall test was performed to detect monotonic trends for variables such as total precipitation, number of days with precipitation greater than 1, 20 or 40 mm, number of consecutive dry days and number of consecutive wet days. Moreover, the linear tendency was calculated and accepted (or rejected) by performing a Student's t-test of the calculated residual values. The results showed, on average, a clear change in precipitation patterns for the last 20 years. Annual rainfall had decreased, with values ranging from 0 to 15%, dry periods had increased in length, and the number of rainy days had decreased (especially when high daily precipitation was considered). This latter notion did not exactly concur with the conclusions of the Intergovernmental Panel on Climate Change (IPCC) 2014 Synthesis Report related to the study area. [ABSTRACT FROM AUTHOR]

The 2015 release of the precipitation climatology from the Global Precipitation Climatology Centre (GPCC) for 1951-2000, based on climatological normals of about 75,100 rain gauges, allows for quantification of mean land surface precipitation as part of the global water cycle. In GPCC's 2011-release, a bulk climatological correction was applied to compensate for gauge undercatch. In this paper we derive an improved correction approach based on the synoptic weather reports for the period 1982-2015. The compared results show that the climatological approach tends to overestimate the correction for Central and Eastern Europe, especially in the northern winter, and in other regions throughout the year. Applying the mean weather-dependent correction to the GPCC's uncorrected precipitation climatology for 1951-2000 gives a value of 854.7 mm of precipitation per year (excluding Antarctica) or 790 mm for the global land surface. The warming of nearly 1 K relative to pre-industrial temperatures is expected to be accompanied by a 2%-3% increase in global (land and ocean) precipitation. However, a comparison of climatology for 30-year reference periods from 1931-1960 up to 1981-2010 reveals no significant trend for land surface precipitation. This may be caused by the large variability of precipitation, the varying data coverage over time and other issues related to the sampling of rain-gauge networks. The GPCC continues to enlarge and further improve the quality of its database, and will generate precipitation analyses with homogeneous data coverage over time. Another way to reduce the sampling issues is the combination of rain gauge-based analyses with remote sensing (i.e., satellite or radar) datasets. [ABSTRACT FROM AUTHOR]

Interest in the impacts of climate change is ever increasing. This is particularly true of the water sector where understanding potential changes in the occurrence of both floods and droughts is important for strategic planning. Climate variability has been shown to have a significant impact on UK climate and accounting for this in future climate change projections is essential to fully anticipate potential future impacts. In this paper a new resampling methodology is developed which includes the variability of both baseline and future precipitation. The resampling methodology is applied to 13 CMIP3 climate models for the 2080s, resulting in an ensemble of monthly precipitation change factors. The change factors are applied to the Eden catchment in eastern Scotland with analysis undertaken for the sensitivity of future river flows to the changes in precipitation. Climate variability is shown to influence the magnitude and direction of change of both precipitation and in turn river flow, which are not apparent without the use of the resampling methodology. The transformation of precipitation changes to river flow changes display a degree of non-linearity due to the catchment's role in buffering the response. The resampling methodology developed in this paper provides a new technique for creating climate change scenarios which incorporate the important issue of climate variability. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP). [ABSTRACT FROM AUTHOR]

The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project's strategy is to apply prescribed observationally based anomaly forcing, i.e., 'idealized but realistic' forcing, in simulations by climate models. The goal is to assess these forcings' effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the twentieth century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60 % of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40 % of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought. [ABSTRACT FROM AUTHOR]

The analysis of the tree radial growth response to climate is crucial for dendroclimatological research. However, the response relationships between tree-ring indices and climatic factors at different timescales are not yet clear. In this study, the tree-ring width of Huashan pine (Pinus armandii) from Huashan in the Qinling Mountains, north-central China, was used to explore the response differences of tree growth to climatic factors at daily, pentad (5 days), dekad (10 days) and monthly timescales. Correlation function and linear regression analysis were applied in this paper. The tree-ring width showed a more sensitive response to daily and pentad climatic factors. With the timescale decreasing, the absolute value of the maximum correlation coefficient between the tree-ring data and precipitation increases as well as temperature (mean, minimum and maximum temperature). Compared to the other three timescales, pentad was more suitable for analysing the response of tree growth to climate. Relative to the monthly climate data, the association between the tree-ring data and the pentad climate data was more remarkable and accurate, and the reconstruction function based on the pentad climate was also more reliable and stable. We found that the major climatic factor limiting Huashan pine growth was the precipitation of pentads 20–35 (from April 6 to June 24) rather than the well-known April–June precipitation. The pentad was also proved to be a better timescale for analysing the climate and tree growth in the western and eastern Qinling Mountains. The formation of the earlywood density of Chinese pine (Pinus tabulaeformis) from Shimenshan in western Qinling was mainly affected by the maximum temperature of pentads 28–32 (from May 16 to June 9). The maximum temperature of pentads 28–33 (from May 16 to June 14) was the major factor affecting the ring width of Chinese pine from Shirenshan in eastern Qinling. [ABSTRACT FROM AUTHOR]

Few if any high-resolution (annually resolved) paleoclimate records are available for the Hawaiian Islands prior to ~1850 CE, after which some instrumental records start to become available. This paper shows how atmospheric teleconnection patterns between North America and the northeastern North Pacific (NNP) allow for reconstruction of Hawaiian Islands rainfall using remote proxy information from North America. Based on a newly available precipitation dataset for the state of Hawaii and observed and reconstructed December-February (DJF) sea level pressures (SLPs) in the North Pacific Ocean, the authors make use of a strong relationship between winter SLP variability in the northeast Pacific and corresponding DJF Hawaii rainfall variations to reconstruct and evaluate that season's rainfall over the period 1500-2012 CE. A general drying trend, though with substantial decadal and longer-term variability, is evident, particularly during the last ~160 years. Hawaiian Islands rainfall exhibits strong modulation by El Niño-Southern Oscillation (ENSO), as well as in relation to Pacific decadal oscillation (PDO)-like variability. For significant periods of time, the reconstructed large-scale changes in the North Pacific SLP field described here and by construction the long-term decline in Hawaiian winter rainfall are broadly consistent with long-term changes in tropical Pacific sea surface temperature (SST) based on ENSO reconstructions documented in several other studies, particularly over the last two centuries. Also noted are some rather large multidecadal fluctuations in rainfall (and hence in NNP SLP) in the eighteenth century of undetermined provenance. [ABSTRACT FROM AUTHOR]

The present paper provides a scientific assessment of the inter-decadal variability of the Asian summer monsoon and its significant effect on precipitation patterns in China. The paper consists of four parts. The first part shows the observed inter-decadal weakening of the Asian summer monsoon since the end of 1970s. The second part describes the impact of the weakening Asian summer monsoon on precipitation in China, which caused the major monsoon rainbelt to shift southward from North China to South China over about 30 years, with a 60-80 oscillation dominating. The third part projects the future change of the Asian summer monsoon in the warmer climate by using IPCC AR4 climate models. It has been found that major monsoon rainfall belts in East Asia will move northward in the 2040s, with summer precipitation in North China increasing considerably and stably. Finally, in the fourth part, the implication of the adaptation to the above climate change and related water management will be discussed including the water transport engineering project from the Yangtze River to the Yellow River and North China. [ABSTRACT FROM AUTHOR]

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers. [ABSTRACT FROM AUTHOR]

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

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)-two contrasting SD methods-in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector. [ABSTRACT FROM AUTHOR]

This paper analyzes climatological characteristics and temporal variation of the start and end dates of the rainy season over East Asia using a daily precipitation dataset for the time period 1951-2009. The rainy season is defined by a 5-day rainfall standard, and the regional average time series is constructed by applying the weighted-average method. Results show that the rainy season starts later, ends earlier and lasts shorter from southeast to northwest. In spring, the rainy belt slowly moves northward from 30 to 33°N in China and from 33 to 36°N on the Korean Peninsula and Japanese Islands. From 1951 to 2009, the rainy season generally began earlier in China but later in Korea and Japan, and it ended earlier at latitudes north of 35°N and later south of 35°N. The region-averaged start and end dates of the rainy season in the study region insignificantly advanced, and the duration of the rainy season insignificantly increased from 1951 to 2009. The rainy season duration slightly decreased in the Russian Far East and in northern and western China, and significantly decreased on the Korean Peninsula and the southern Hokkaido Islands, though it obviously increased in the Yangtze--Huaihe River Basins. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper revisits the strength and stationarity of linear statistical relationships between indices of monsoon rainfall derived from rain gauge data for three regions in tropical West Africa [West Sahel (WS), Central Sahel (CS), and Guinea Coast (GC)] and various indices describing remote variations in the ocean-atmosphere climate systems from 1921 to 2009. The results reveal that both the Atlantic Multi-decadal Oscillation ( AMO) and the Atlantic Meridional Mode ( AMM) show a positive linear correlation to Sahel rainfall. The percent variance explained ( PVE) ranges from 10 to 25%. The correlation stems from periods longer than 8 years for AMO, but AMM does show a correlation on interannual time scales for WS that was absent in the 1970s and 1980s. The PVE by El Niño Southern Oscillation ( ENSO) indices is, though statistically significant, on the order of 10% and found mainly on the interannual time scale. A strong and stable correlation with PVEs larger than 50% is found between the sea surface temperatures (SSTs) in the Atlantic 3 region ( ATL3, 0°-20°W, 3°S-3°N) and the GC rainfall. On the other hand, the correlation between ATL3 and WS rainfall changed from significantly negative to significantly positive after the 1970s. The Eastern Mediterranean SSTs are found to be significantly related to CS rainfall, especially in recent years with PVEs between 36 and 47%. Multi-linear regression analyses reveal that the relative importance of the Indian Ocean is 42% in the optimal regression model. For the CS, this value is 37% for the Eastern Mediterranean SSTs and 70% in case of the GC using the ATL3 index. However, except for the GC, non-stationarities in the correlation between the climate state indexes and West African monsoon ( WAM) rainfall suggest the need of the application of different regression models depending on the active 'teleconnection regime'. [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]

Based on numerical experiments using the NCAR CAM3-CLM3 models, this paper examines the impact of soil moisture, vegetation, and sea surface temperature (SST) on the inter-annual variability of climate over land. For each element, two experiments are carried out, with the inter-annual variability preserved in one experiment and eliminated in the other. Differences in the standard deviation of the precipitation and air temperature at the inter-annual time scale are used to quantify the impacts from soil moisture dynamics, vegetation dynamics, and oceanic forcing. The impact of oceanic forcing is mainly limited to the Tropics, with the strongest signal in the equatorial zone, and moisture convergence is the key linkage between SST forcing and tropical precipitation. Soil moisture plays a significant role in climate variability during the rainy seasons of all semi-arid regions (which is consistent with many previous studies), and during the dry seasons of the humid Amazon. Evapotranspiration is identified as the main mechanism linking precipitation variability to soil moisture. Amazon is the only region where vegetation dynamics has a significant influence on precipitation variability. However, the impact of vegetation dynamics on temperature is strong over the US Great Plains in all four seasons and in the Amazon region during the dry and dry-to-wet transition seasons. [ABSTRACT FROM AUTHOR]

On a hemispheric scale, it is now well established that stratospheric ozone depletion has been the principal driver of externally forced atmospheric circulation changes south of the Equator in the last decades of the 20th Century. The impact of ozone depletion has been felt over the entire hemisphere, as reflected in the poleward drift of the midlatitude jet, the southward expansion of the summertime Hadley cell and accompanying precipitation trends deep into the subtropics. On a regional scale, however, surface impacts directly attributable to ozone depletion have yet to be identified. In this paper we focus on South Eastern South America (SESA), a region that has exhibited one of the largest wetting trends during the 20th Century. We study the impact of ozone depletion on SESA precipitation using output from 6 different climate models, spanning a wide range of complexity. In all cases we contrast pairs of model integrations with and without ozone depletion, but with all other forcings identically specified. This allows for unambiguous attribution of the computed precipitation trends. All 6 climate models consistently reveal that stratospheric ozone depletion results in a significant wetting of SESA over the period 1960-1999. Taken as a whole, these model results strongly suggest that the impact of ozone depletion on SESA precipitation has been as large as, and quite possibly larger than, the one caused by increasing greenhouse gases over the same period. [ABSTRACT FROM AUTHOR]

Daily precipitation and temperature records at 13 stations for the period 1960-2008 were analyzed to identify climatic change and possible effects of urbanization on low-temperature precipitation [LTP, precipitation of ⩾ 0.1 mm d occurring under a daily minimum temperature (Tmin) of ⩽ 0°C] in the greater Beijing region (BJR), where a rapid process of urbanization has taken place over the last few decades. The paper provides a climatological overview of LTP in BJR. LTP contributes 61.7% to the total amount of precipitation in BJR in the cold season (November-March). There is a slight increasing trend [1.22 mm (10 yr)] in the amount of total precipitation for the cold season during 1960-2008. In contrast, the amount of LTP decreases by 0.6 mm (10 yr). The warming rate of Tmin in BJR is 0.66°C (10 yr). Correspondingly, the frequency of LTP decreases with increasing Tmin by −0.67 times per °C. The seasonal frequency and amount of LTP in southeast BJR (mostly urban sites) are 17%-20% less than those in the northwestern (rural and montane sites). The intensity of LTP for the urban sites and northeastern BJR exhibited significant enhancing trends [0.18 and 0.15 mm d (10 yr), respectively]. The frequency of slight LTP (<0.2 mm d) significantly decreased throughout BJR [by about 5.74% (10 yr) in the urban area and northeast BJR], while the contribution of the two heaviest LTP events to total LTP amount significantly increased by 3.2% (10 yr). [ABSTRACT FROM AUTHOR]

The possible change in the characteristics of weather in the future should be considered as important as the mean climate change because the increasing risk of extremes is related to the variability on daily time scales. The weather characteristics can be represented by the climatological mean interdiurnal (day-to-day) variability (MIDV). This paper first assessed the phase five of the Coupled Model Intercomparison Project coupled climate models’ capability to represent MIDV for the surface maximum and minimum temperature, surface wind speed and precipitation under the present climate condition. Based on the assessment, we selected three best models for projecting future change. We found that the future changes in MIDV are characterized by: (a) a marked reduction in surface maximum and minimum temperature over high latitudes during the cold season; (b) a stronger reduction in the surface minimum temperature than in the maximum temperature; (c) a reduction in surface wind speed over large parts of lands in Northern Hemisphere (NH) during NH spring; (d) a noticeable increase in precipitation in NH mid-high latitudes in NH spring and winter, and in particular over East Asia throughout most of the year. [ABSTRACT FROM AUTHOR]

Ongoing global warming not only involves changes in temperature and the global mean; it affects more or less every part of the climate. Regional temperature changes are often greater or smaller than corresponding changes in the global mean. In some cases the direction of change may also be different. For example, temperature changes are higher over land than over the ocean. Precipitation increases in some regions but decreases in others. Changes in extreme events may differ from changes in the corresponding mean. Present scientific knowledge clearly indicates that the already observed global trend towards warmer conditions will continue and that it will be accompanied by changes in yet other aspects of climate. This paper highlights, as a brief review, aspects of our changing climate from the available scientific knowledge with a bearing on the energy sector. Its focus is on temperature and precipitation, with some consideration of wind and sea level, among others. While uncertainties remain as far as the magnitude of future global-scale changes is concerned, and even more so their many regional patterns, significant changes are foreseen in, for example, global and regional temperature and precipitation, sea level rise, and in the characteristics of various extreme events. [ABSTRACT FROM AUTHOR]

This is the second of the two-part paper series on the analysis and evaluation of the Fifth phase of Coupled Model Intercomparison Project (CMIP5) simulation of contemporary climate as well as IPCC, AR5 Representative Concentrations Pathways (RCP), 4.5 and 8.5 scenarios projections of the Greater Horn of Africa (GHA) Climate. In the first part (Otieno and Anyah in Clim Dyn, ) we focused on the historical simulations, whereas this second part primarily focuses on future projections based on the two scenarios. Six Earth System Models (ESMs) from CMIP5 archive have been used to characterize projected changes in seasonal and annual mean precipitation, temperature and the hydrological cycle by the middle of twenty-first century over the GHA region, based on IPCC, 5th Assessment Report (AR5) RCP4.5 and RCP8.5 scenarios. Nearly all the models outputs analyzed reproduce the correct mean annual cycle of precipitation, with some biases among the models in capturing the correct peak of precipitation cycle, more so, March-April-May (MAM) seasonal rainfall over the equatorial GHA region. However, there is significant variation among models in projected precipitation anomalies, with some models projecting an average increase as others project a decrease in precipitation during different seasons. The ensemble mean of the ESMs indicates that the GHA region has been experiencing a steady increase in both precipitation and temperature beginning in the early 1980s and 1970s respectively in both RCP4.5 and RCP8.5 scenarios. Going by the ensemble means, temperatures are projected to steadily increase uniformly in all the seasons at a rate of 0.3/0.5 °C/decade under RCP4.5/8.5 scenarios over northern GHA region leading to an approximate temperature increase of 2/3 °C by the middle of the century. On the other hand, temperatures will likely increase at a rate of 0.3/0.4 °C/decade under RCP4.5/8.5 scenarios in both equatorial and southern GHA region leading to an approximate temperature increase of 2/2.5 °C by the middle of twenty-first century. Nonetheless, projected precipitation increase varied across seasons and sub-regions. With the exception of the equatorial region, that is projected to experience precipitation increase during DJF season, most sub-regions are projected to experience precipitation increase within their peak seasons, with the highest rate of increase experienced during DJF and OND seasons over southern and equatorial GHA regions respectively. Notably, as precipitation increases, the deficit (E < P) between evaporation (E) and precipitation (P) increased over the years, with a negatively skewed distribution. This generally implies that there is a high likelihood of an increased deficit in local moisture supply. This remarkable change in the general hydrological cycle (i.e. deficit in local moisture) is projected to be also coincident with intensified westerly anomaly influx from the Congo basin into the region. However, better understanding of the detailed changes in hydrological cycle will require comprehensive water budget analyses that require daily or sub-daily variables, and was not a specific focus of the present study. [ABSTRACT FROM AUTHOR]

This paper adopts a technique common in the dynamical climate modelling literature, that of pattern scaling, and applies it to previously available statistically downscaled station level data for Ireland for two climatically relevant variables, that of temperature and precipitation. This technique allows for the rapid development of climate scenarios for additional emissions scenarios not previously available from the GCM modelling centres. Having derived the end of century (2080s) change in both these variables for four marker emissions scenarios (A1FI, A2, B2, B1), regional response rates, or the regional rate of warming per °C global warming at each station, were calculated. The estimated ranges in regional responses at each station were then compared to regional response rates for the Irish ‘grid box’ derived from a larger sample of 14 GCMs, in order to determine if the calculated response rates were illustrative of a wider suite of GCMs. A Monte Carlo (MC) resampling approach was then employed to sample regional response rates for selected stations and for different estimates of future warming. On the basis of the MC approach, probability distribution functions (pdfs) of simulated changes in temperature and precipitation were constructed and compared to the original statistically downscaled data. The methodology and results presented represent a significant contribution to the traditional approach of statistical downscaling through the development of associated likelihoods, rather than just a change in the mean value. While the methodology presented should enable the rapid development of probabilistic based climate projections, based on a limited availability of downscaled climate scenarios, caution needs to be exercised in the interpretation of the results. While they provide a basis for risk or policy assessment, estimates of the level of risk are not independent of the method employed. [ABSTRACT FROM AUTHOR]

Mountain observatories around the world are unique sites for monitoring and investigating variations, trends, forcings and feedbacks in the climate system, which are of utmost interest for understanding global climate change. From the small number of these research platforms, Sonnblick Observatory (Austrian Alps) stands out because of its long time series dating back to 1886 at an elevation of 3,100 m a.s.l. The paper describes the contribution of mountain observatories to climatology by the example of Sonnblick Observatory. The observatory's scientific evolution is summarised, starting from the original idea of upper air atmospheric measurements to its recent role as an atmospheric background station and interdisciplinary research site. [ABSTRACT FROM AUTHOR]

Over recent decades, analyses of the structure and impact of atmospheric teleconnections have substantially increased our understanding of the climate system and the role of climate variability. Moving beyond simple correlations between teleconnection indices and temperature and precipitation anomalies, synoptic climatology has been able to provide insight on the spatiotemporal manifestation of teleconnection anomalies, as well as further understanding in terms of teleconnection-related anomalies in circulation pattern frequencies that can lead to extreme events over individual areas. In this progress report, we focus on a number of recent papers that, broadly defined, assess two realms of teleconnections: the North Atlantic circulation, largely focusing on the North Atlantic Oscillation; and the north and tropical Pacific circulation, as manifest in El Niño–Southern Oscillation, the Pacific North American pattern, and the Pacific Decadal Oscillation. In each of these two regions, we highlight the major goals and results of recent synoptic research. [ABSTRACT FROM PUBLISHER]

The current global climate conditions differ from the historical average data. In the Arid Chaco this trend is reflected in the advance of the agricultural frontier towards the east of the subregion, due partially to an increase in the precipitation. The aim of this paper is to analyze temporal changes of temperature and precipitation, and the ENSO influence over these variables. The historical series of the Arid Chaco climate were analyzed for six locations for the period 1931-2011. The results show positive trends in precipitation and average temperatures, although increasingly variable. Precipitations and temperature are inversely related, possibly as a result of the advance of cold fronts from the south, causing precipitation. Nevertheless, the linear regressions between both variables do not fit significantly (p<0,05). Changes in the location of high and low pressure centers in the subregion explain another part of the variation in mean annual temperatures and precipitation. Many of the observed climate anomalies coincide with ENSO events, although the intensity of these events have little relation with the intensity of climate anomalies in the subregion. [ABSTRACT FROM AUTHOR]

This paper addresses several hypotheses designed to explain why AOGCM simulations of future climate in the third phase of the Coupled Model Intercomparison Project (CMIP3) feature an intensified reduction of precipitation over the Meso-America (MA) region. While the drying is consistent with an amplification of the subtropical high pressure cells and an equatorward contraction of convective regions due to the ''upped ante'' for convection in a warmer atmosphere, the physical mechanisms behind the intensity and robustness of the MA drying signal have not been fully explored. Regional variations in sea surface temperature (SST) warming may play a role. First, SSTs over the tropical North Atlantic (TNA) do not warm as much as the surrounding ocean. The troposphere senses a TNA that is cooler than the tropical Pacific, potentially exciting a Gill-type response, increasing the strength of the North Atlantic subtropical high. Second, the warm ENSO-like state simulated in the eastern tropical Pacific could decrease precipitation over MA, as warm ENSO events are associated with drying over MA. The authors use the International Centre for Theoretical Physics (ICTP) AGCM to investigate the effects of these regional SST warming variations on the projected drying over MA. First, the change of SSTs [Special Report on Emissions Scenarios (SRES) A1B''s Twentieth-Century Climate in Coupled Model (A1B-20C)] in the ensemble average of the CMIP3 models is applied to determine if the ICTP AGCM can replicate the future drying. Then the effects of 1) removing the reduced warming over the TNA, 2) removing the warm ENSO-event-like pattern in the eastern tropical Pacific, and 3) applying uniform SST warming throughout the tropics are tested. The ICTP AGCM can reproduce the general pattern and amount of precipitation over MA. Simulations in which the CMIP3 A1B-20C ensemble-average SSTs are added to climatological SSTs show drying of more than 20%% over the MA region, similar to the CMIP3 ensemble average. Replacing the relatively cooler SSTs over the TNA excites a Gill response consistent with an off-equatorial heating anomaly, showing that the TNA relative cooling is responsible for about 16%% (31%%) of the drying in late spring (early summer). The warm ENSO-like SST pattern over the eastern Pacific also affects precipitation over the MA region, with changes of 19%% and 31%% in March--June (MMJ) and June--August (JJA), respectively. This work highlights the importance of understanding even robust signals in the CMIP3 future scenario simulations, and should aid in the design and analysis of future climate change studies over the region. [ABSTRACT FROM AUTHOR]

The study of the temporal-spatial variability of dry sequences and the probability of their occurrence are particularly important in understanding the impact of climate change on droughts. This paper analyses the different properties of dry sequences, focussing on extreme condition, analysing their degree of spatial coherence, and their temporal variability. For the study, daily precipitation data are used for the period 1961-2000 throughout Argentina. The region north of 40°S is divided in two from the meridian of 63°W with highly differentiated 'dry' properties. The eastern region is more homogeneous where mean dry sequences last less than six days and long sequences of about 60 days. The Andean region shows a marked east-west gradient in any of the above properties, with extreme values of over 10 days (mean sequences) and 150 days (the longest sequences). At the seasonal level the above properties reflect a differential pattern according to the time of year under study. In the case of summer, when the dry sequences can be more harmful to crops, the maximum duration in the Pampa region can extend to about 25 days. Owing to the problems that dry sequences of over 30 days can produce in the different ecosystems and based on the above findings, the temporal-spatial variability is analysed, and a variability over the years, together with a progressive decrease in the occurrence of such events, is found. The study of extreme dry events provides useful tools that can be applied to different hydrological and agricultural needs. Furthermore, our climatological findings help validate climate models. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper presents a validation study for a high-resolution version of the Regional Climate Model version 3 (RegCM3) over the Carpathian basin and its surroundings. The horizontal grid spacing of the model is 10 km--the highest reached by RegCM3. The ability of the model to capture temporal and spatial variability of temperature and precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal (daily to climatology) and spatial (inner domain average to local) scales against different observational datasets. The simulated period is 1961--90. RegCM3 shows small temperature biases but a general overestimation of precipitation, especially in winter; although, this overestimate may be artificially enhanced by uncertainties in observations. The precipitation bias over the Hungarian territory, the authors'' main area of interest, is mostly less than 20%%. The model captures well the observed late twentieth-century decadal-to-interannual and interseasonal variability. On short time scales, simulated daily temperature and precipitation show a high correlation with observations, with a correlation coefficient of 0.9 for temperature and 0.6 for precipitation. Comparison with two Hungarian station time series shows that the model performance does not degrade when going to the 10-km gridpoint scale. Finally, the model reproduces the spatial distribution of dry and wet spells over the region. Overall, it is assessed that this high-resolution version of RegCM3 is of sufficiently good quality to perform climate change experiments over the Carpathian region--and, in particular, the Hungarian territory--for application to impact and adaptation studies. [ABSTRACT FROM AUTHOR]

Overlying the challenge of managing within natural hydroclimatic variability is the likely modification of runoff variability along with average runoff due to anthropogenic enhancement of greenhouse gas concentrations. In this paper analytical models are developed in which runoff mean and variability, the latter defined by the variance (or standard deviation) of annual runoff, are related to the variances and the covariance of annual precipitation and potential evapotranspiration, and the aridity index (mean annual potential evapotranspiration divided by mean annual precipitation). The method was validated using observed runoff data for 699 worldwide catchments. It was concluded that combining the Schreiber function, which relates the ratio of annual actual evapotranspiration to annual precipitation, with the analytical models provided satisfactory estimates of observed annual runoff mean and interannual variability. It was also concluded that estimates of annual runoff variability based on the simplified model of Koster and Suarez were unsatisfactory. By way of illustrating the new methodology, the approach was applied to projected annual values of precipitation from the Hadley Centre Global Environment Model version 1 (HadGEM) and it showed that considerable changes in reservoir yield are likely to occur if climate change projections of precipitation from HadGEM are realistic. Finally, further simplifications of the equations, based on the Schreiber function, are developed to estimate the mean and standard deviation of annual runoff that allow climate analysts to estimate the impact of potential climate changes on annual runoff characteristics and reservoir yield performance without having to resort to the calibration and application of a rainfall-runoff model or rely on the runoff output from general circulation models to examine such characteristics. [ABSTRACT FROM AUTHOR]

The detection of possible changes in extreme climate events, in terms of the frequency, intensity as well as duration assumes profound importance on the local, regional, and national scales, due to the associated critical socioeconomic consequences. Therefore, an attempt ismade in this paper to evaluate various aspects of future projections of precipitation extremes over India, as projected by a state-of-art regional climatemodeling system, known as PRECIS (Providing REgional Climates for Impacts Studies) towards the end of the 21st century (that is, 2071-2100) using standardized indices. Study reveals that PRECIS simulations under scenarios of increasing greenhouse gas concentration and sulphate aerosols indicate marked increase in precipitation towards the end of the 21st century and is expected to increase throughout the year. However the changes in daily precipitation and the precipitation extremes during summer monsoon (June through September) season are prominent than during the rest of year. PRECIS simulations under both A2 and B2 scenarios indicate increase in frequency of heavy precipitation events and also enhancement in their intensity towards the end of the 21st century. Both A2 and B2 scenarios show similar patterns of projected changes in the precipitation extremes towards the end of the 21st century. However, the magnitudes of changes in B2 scenario are on the lower side. [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]

Recent studies have shown that changes in global mean precipitation are larger for solar forcing than for CO2 forcing of similar magnitude. In this paper, we use an atmospheric general circulation model to show that the differences originate from differing fast responses of the climate system. We estimate the adjusted radiative forcing and fast response using Hansen’s “fixed-SST forcing” method. Total climate system response is calculated using mixed layer simulations using the same model. Our analysis shows that the fast response is almost 40% of the total response for few key variables like precipitation and evaporation. We further demonstrate that the hydrologic sensitivity, defined as the change in global mean precipitation per unit warming, is the same for the two forcings when the fast responses are excluded from the definition of hydrologic sensitivity, suggesting that the slow response (feedback) of the hydrological cycle is independent of the forcing mechanism. Based on our results, we recommend that the fast and slow response be compared separately in multi-model intercomparisons to discover and understand robust responses in hydrologic cycle. The significance of this study to geoengineering is discussed. [ABSTRACT FROM AUTHOR]

This paper introduces an original method for climate change detection, called temporal optimal detection method. The method consists in searching for a smooth temporal pattern in the observations. This pattern can be either the response of the climate system to a specific forcing or to a combination of forcings. Many characteristics of this new method are different from those of the classical “optimal fingerprint” method. It allows to infer the spatial distribution of the detected signal, without providing any spatial guess pattern. The spatial properties of the internal climate variability doesn’t need to be estimated either. The estimation of such quantities being very challenging at regional scale, the proposed method is particularly well-suited for such scale. The efficiency of the method is illustrated by applying it on real homogenized datasets of temperatures and precipitation over France. A multimodel detection is performed in both cases, using an ensemble of atmosphere-ocean general circulation models for estimating the temporal patterns. Regarding temperatures, new results are highlighted, especially by showing that a change is detected even after removing the uniform part of the warming. The sensitivity of the method is discussed in this case, relatively to the computation of the temporal patterns and to the choice of the model. The method also allows to detect a climate change signal in precipitation. This change impacts the spatial distribution of the precipitation more than the mean over the domain. The ability of the method to provide an estimate of the spatial distribution of the change following the prescribed temporal patterns is also illustrated. [ABSTRACT FROM AUTHOR]

Vegetation around the Daihai Lake, northern China, is very sensitive to climate changes. In this paper, pollen-based quantitative climate reconstructions using three methods [weighted averaging partial least squares method (WAPLS), modern analog technique (MAT), and pollen response surface method (PRS)] were conducted to obtain robust reconstructions of Holocene climate changes in the Daihai Lake area. The result obtained by the three methods all consistently show the annual precipitation to have been 50–100 mm lower in the early Holocene, 100–200 mm higher in the Mid-Holocene, and 50–100 mm lower again in the late Holocene than at present. The WAPLS and the MAT methods also show quasi-synchronous oscillations of the mean annual temperature (Ta); 1°–2°C lower in the Early Holocene and 1°–3°C higher in the Mid-Holocene than today. The time period from 6200 to 5100 cal yr BP was the wettest and the warmest interval, with an annual precipitation (Pa) greater than 550 mm and mean annual temperature Ta higher than 6.5°C. Several cold and dry events can be identified to occur about 8200, 6000, and 4400 cal yr BP, with an annual precipitation less than 400 mm and a mean annual temperature colder than 4.5°C, respectively. The mean temperature of the warmest month (Tw) as reconstructed using both WAPLS and MAT methods was relatively stable during the Holocene, fluctuating about ±2°C relative to the present level, but the PRS method suggests more varied Tw values in both amplitude and frequency. After 1500 cal yr BP, no consistent pattern can be observed from these three different analyses, probably because of the impact of intensified human disturbances on the natural vegetation. The fluctuations of annual precipitation (Pa) correspond to that observed in Dongge Cave in southern China. The differences might be linked to Indian monsoon and East Asia monsoon climates or caused by the different degree of dating precision, different temporal resolution, and different sensitive response of climate proxies to the climate variations. [ABSTRACT FROM AUTHOR]

Snow is a major determinant of forage availability for reindeer and caribou (Rangifer tarandus; hereafter Rangifer) in winter and is, consequently, a medium through which climate variation may influence population dynamics in this species. Periodic "icing" of winter ranges, where interludes of mild weather result in formation of crusted snow and basal ice that restrict access to forage, is held to be a cause of mass starvation, catastrophic declines in numbers, and even extirpation of local populations. It has been suggested that warming of the Arctic may result in increased frequency of winters with unfavorable snow and ice conditions, with serious consequences for Rangifer. This paper examines data on major declines in populations of Rangifer to determine the mechanism(s) of these events and the role of snow and ice conditions in them. Thirty-one declines, involving numerical decreases between 25% and 99%, were identified in 12 populations. Declines were of two types: the negative phase of irruptive oscillations, mainly associated with populations introduced into new habitat, and numerical fluctuation in persistently unstable established populations. The mechanisms of decline differed widely in both categories, ranging from wholly mortality to almost wholly emigration. In all cases, the observed dynamics are best interpreted as a product of interaction between internal processes (density dependence) and the external abiotic conditions (density independence). The strength and the form of density independence, parameterized in terms of local weather or large-scale climate, varies widely between populations, reflecting the enormous range of climate conditions across the circumpolar distribution of Rangifer. This complicates the search for abiotic components likely to be consistently important determinants of population growth in the species. There are few data demonstrating the presence of extensive hard snow or basal ice on ranges during winter(s) in which populations declined, and none confirming ice as a ubiquitous and potent agent in the dynamics of Rangifer. Instead, where the simultaneous effects of density-dependent and density-independent factors are examined across the full temporal record of dynamics, climatic conditions associated with increased amounts of snow or winter warming are generally found to enhance the abundance of animals, at least in established populations. [ABSTRACT FROM AUTHOR]

This paper discusses patterns of annual and monthly precipitation variability at seven weather stations in east central Europe (1851–2007). Precipitation patterns were compared to three simple regional indices of atmospheric circulation, i.e., western circulation, southern circulation and the cyclonicity (C) index and a relationship between precipitation and the North Atlantic Oscillation index was identified. Correlations of the monthly records and multiple regression, using a principal components’ analysis, helped determine the statistical significance of the dependence of precipitation on the circulation indices. The Mann–Kendall test revealed no trend to change in any of the precipitation series, but a certain spatial regularity could be discerned in the phase of the annual periodic component. A common feature of the variability in central European annual precipitation is the dry period identified in the 1980s and the first half of the 1990s. In the northern part of the region, above-average precipitation was noted from the 1960s through to the mid-1970s as a result of the frequent prevalence of depressions. South of the divide, the wettest period was recorded at the turn of 1930s/1940s. After a number of very wet years in the last decade of the twentieth century and the beginning of the twenty-first century, precipitation began to fall at all of the region’s weather stations. The C index is the strongest circulation-linked factor influencing precipitation in central Europe and it accounts for more than 40% of the variance in spatially averaged wintertime precipitation. [ABSTRACT FROM AUTHOR]

Climate models suggest that anthropogenic emissions are likely to induce an important drying during summer over most of Europe in the late 21st century. However, the amplitude of the associated decrease in precipitation strongly varies among the different climate models. In order to reduce this spread, it is first necessary to identify its causes and the associated physical mechanisms. Consequently, the focus of this paper is to better estimate the role of large scale circulation (LSC) in precipitation changes over Europe using a multi-model framework and then to characterize the LSC changes using the weather regime paradigm. We show that LSC changes directly lead to a decrease of precipitation over northwestern Europe. This circulation-driven decrease in rainfall is mainly linked to an increase (decrease) of the occurrence of positive (negative) phase of the North Atlantic Oscillation regime. LSC is also responsible for a significant part of the models spread in precipitation changes over these regions. Over southern Europe, the role of LSC changes on multi-model mean precipitation changes is generally weak. We also show that the precipitation anomalies directly induced by LSC modifications seem to be further amplified through local feedbacks. [ABSTRACT FROM AUTHOR]

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

Drastic changes were detected in glacial systems of the Antarctic Peninsula in the last decades. The observed phenomena comprise the disintegration of ice shelves, acceleration and thinning of glaciers, and retreat of glacier fronts. However, due to the lack of consistent systematic observations in particular of the higher parts of the glacial systems, it is difficult to predict further responses of the Antarctic Peninsula glaciers to climatic change. The present paper analyses spatial and temporal variations of changes in the dry-snow line altitude on the Antarctic Peninsula as extracted from a time series (1992–2005) of ERS-1/2 SAR and Envisat ASAR data. Upward changes in dry-snow line altitude were observed in general, and are attributed to extreme high-temperature events impacting the central plateaus of the Antarctic Peninsula and the increasing duration of warming periods. A mean decrease in dry-snow line altitude was detected on the west side of the peninsula and is identified as a response to recorded increase in precipitation and accumulation. These results validate the capability of SAR data for deriving superficial parameters of glaciers to be used as indicators of climatic changes in high-latitude regions where operational restrictions limit conventional meteorological observations. [ABSTRACT FROM AUTHOR]

A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, -0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are dynamical downscaling tools aimed to improve the modelling of local physical processes. Ensembles of RCMs are widely used to improve the coarse-grain estimates of global climate models (GCMs) since the use of several RCMs helps to palliate uncertainties arising from different dynamical cores and numerical schemes methods. In this paper, we analyse the differences and similarities in the climate change response for an ensemble of heterogeneous RCMs forced by one GCM (HadAM3H), and one emissions scenario (IPCC's SRES-A2 scenario). As a difference with previous approaches using PRUDENCE database, the statistical description of climate characteristics is made through the spatial and temporal aggregation of the RCMs outputs into probability distribution functions (PDF) of monthly values. This procedure is a complementary approach to conventional seasonal analyses. Our results provide new, stronger evidence on expected marked regional differences in Europe in the A2 scenario in terms of precipitation and temperature changes. While we found an overall increase in the mean temperature and extreme values, we also found mixed regional differences for precipitation. [ABSTRACT FROM AUTHOR]

A government-funded scheme, the UK Climate Change Communications Initiative (UKCCCI), has provided money for organisations to deliver projects that attempt to impact positively on people's attitudes towards climate change. This devolution of communications is a relatively novel approach after previous centralised campaigns. This paper considers what size of target audience is most appropriate for attitude change initiatives. It compares data from a regional UKCCCI project aimed at residents of two counties with a nationally representative dataset. Regional data are also analysed to see if there are differences in attitudes within the two-counties target audience. The study suggests that attitude change interventions must strike a balance between personalisation of information and the higher cost of targeting smaller groups with more specific material. [ABSTRACT FROM AUTHOR]

Reliable projections of future changes in local precipitation extremes are essential for informing policy decisions regarding mitigation and adaptation to climate change. In this paper, the extent to which the natural variability of the climate affects one’s ability to project the anthropogenically forced component of change in daily precipitation extremes across Europe is examined. A three-member ensemble of the Hadley Centre Regional Climate Model (HadRM3H) is used and a statistical framework is applied to estimate the uncertainty due to the full spectrum of climate variability. In particular, the results and understanding presented here suggest that annual to multidecadal natural variability may contribute significant uncertainty. For this ensemble projection, extreme precipitation changes at the grid-box level are found to be discernible above climate noise over much of northern and central Europe in winter, and parts of northern and southern Europe in summer. The ability to quantify the change to a reasonable level of accuracy is largely limited to regions in northern Europe. In general, where climate noise has a significant component varying on decadal time scales, single 30-yr climate change projections are insufficient to infer changes in the extreme tail of the underlying precipitation distribution. In this context, the need for ensembles of integrations is demonstrated and the relative effectiveness of spatial pooling and averaging for generating robust signals of extreme precipitation change is also explored. The key conclusions are expected to apply more generally to other models and forcing scenarios. [ABSTRACT FROM AUTHOR]

Trustworthy probabilistic projections of regional climate are essential for society to plan for future climate change, and yet, by the nonlinear nature of climate, finite computational models of climate are inherently deficient in their ability to simulate regional climatic variability with complete accuracy. How can we determine whether specific regional climate projections may be untrustworthy in the light of such generic deficiencies? A calibration method is proposed whose basis lies in the emerging notion of seamless prediction. Specifically, calibrations of ensemble-based climate change probabilities are derived from analyses of the statistical reliability of ensemble-based forecast probabilities on seasonal time scales. The method is demonstrated by calibrating probabilistic projections from the multimodel ensembles used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), based on reliability analyses from the seasonal forecast Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset. The focus in this paper is on climate change projections of regional precipitation, though the method is more general. [ABSTRACT FROM AUTHOR]