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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]

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]

The objective of this paper is to understand how large-scale processes, cloud cover and surface fluxes affect the temperature variability over the SIRTA site, near Paris, and in a regional climate simulation performed in the frame of HyMeX/Med-CORDEX programs. This site is located in a climatic transitional area where models usually show strong dispersions despite the significant influence of large scale on interannual variability due to its western location. At seasonal time scale, the temperature is mainly controlled by surface fluxes. In the model, the transition from radiation to soil moisture limited regime occurs earlier than in observations leading to an overestimate of summertime temperature. An overestimate of shortwave radiation (SW), consistent with a lack of low clouds, enhances the soil dryness. A simulation with a wet soil is used to better analyse the relationship between dry soil and clouds but while the wetter soil leads to colder temperature, the cloud cover during daytime is not increased due to the atmospheric stability. At shorter time scales, the control of surface radiation becomes higher. In the simulation, higher temperatures are associated with higher SW. A wet soil mitigates the effect of radiation due to modulation by evaporation. In observations, the variability of clouds and their effect on SW is stronger leading to a nearly constant mean SW when sorted by temperature quantile but a stronger impact of cloud cover on day-to-day temperature variability. Impact of cloud albedo effect on precipitation is also compared. [ABSTRACT FROM AUTHOR]

This paper presents a ten-member ensemble of twentieth century Ocean ReAnalyses called ORA-20C. ORA-20C assimilates temperature and salinity profiles and is forced by the ECMWF twentieth century atmospheric reanalysis (ERA-20C) over the 1900-2010 period. This study attempts to identify robust signals of ocean heat content change in ORA-20C and detect contamination by model errors, initial condition uncertainty, surface fluxes and observing system changes. It is shown that ORA-20C trends and variability in the first part of the century result from the surface fluxes and model drift towards a warmer mean state and weak meridional overturning circulation. The impact of the observing system in correcting the mean state causes the deceleration of the warming trend and alters the long-term climate signal. The ensemble spread reflects the long-lasting memory of the initial conditions and the convergence of the system to a solution compatible with surface fluxes, the ocean model and observational constraints. Observations constrain the ocean heat uptake trend in the last decades of the twentieth century, which is similar to trend estimations from the post-satellite era. An ocean heat budget analysis attributes ORA-20C heat content changes to surface fluxes in the first part of the century. The heat flux variability reflects spurious signals stemming from ERA-20C surface fields, which in return result from changes in the atmospheric observing system. The influence of the temperature assimilation increments on the heat budget is growing with time. Increments control the most recent ocean heat uptake signals, highlighting imbalances in forced reanalysis systems in the ocean as well as in the atmosphere. [ABSTRACT FROM AUTHOR]

A systematic comparison of El Niño Southern Oscillation reconstructions during the early to mid-nineteenth century is presented using a range of proxy and documentary sources. Reconstructions of the boreal winter Southern Oscillation Index (SOI) using data from ships’ logbooks presented in a companion paper are evaluated and compared to previous ENSO reconstructions. Comparisons between ENSO reconstructions and the instrumental SOI during a period of overlap (1876-1977) are made. These same proxy and documentary reconstructions are then compared to the logbook-based reconstructions, over 1815-1854. The logbook-based reconstructions compare best with a recent multi-proxy reconstruction that used signals taken from different teleconnection regions, and they have an improved agreement with multi-proxy records compared to a previous attempt to reconstruct the SOI from ships’ logbook data. The logbook-based and the multi-proxy reconstructions are found to capture El Niño events better than La Niña events, and East Pacific El Niño events better than Central Pacific El Niño events, thus suggesting a degree of bias in the historical reconstructions. These findings have important implications for future ENSO reconstructions, with a need for an increased understanding of the effects of different ENSO flavours for future reconstructions. [ABSTRACT FROM AUTHOR]

El Niño Modoki events can be further classified into El Niño Modoki I and II in terms of their opposite impacts on southern China rainfall (Wang and Wang, J Clim 26:1322-1338, 2013) and the Indian Ocean dipole mode (Wang and Wang, Clim Dyn 42:991-1005, 2014). The present paper develops an index to identify the types of El Niño events. The El Niño Modoki II (MII) index is defined as the leading principle component of multivariate empirical orthogonal function analysis of the normalized El Niño Modoki index, Niño4 index and 850 hPa relative vorticity anomalies averaged near the Philippine Sea during autumn. The MII index exhibits dominant variations on interannual (2-3 and 4-5 years) and decadal (10-20 years) timescales. El Niño Modoki II events can be well identified by using the MII index value being larger than 1 standard deviation. Further analyses and numerical model experiments confirm that the MII index can portray the major oceanic and atmospheric features of El Niño Modoki II events. The constructed MII index along with previous ENSO indices can be used for classifying and identifying all types of El Niño events. Because of distinct impacts induced by different types of El Niño events, the implication of the present study is that climate prediction and future climate projection under global warming can be improved by using the MII index and other indices to identify the types of El Niño events. [ABSTRACT FROM AUTHOR]

Climate change impact studies for the Northwest European Shelf (NWES) make use of various dynamical downscaling strategies in the experimental setup of regional ocean circulation models. Projected change signals from coupled and uncoupled downscalings with different domain sizes and forcing global and regional models show substantial uncertainty. In this paper, we investigate influences of the downscaling strategy on projected changes in the physical and biogeochemical conditions of the NWES. Our results indicate that uncertainties due to different downscaling strategies are similar to uncertainties due to the choice of the parent global model and the downscaling regional model. Downscaled change signals reveal to depend stronger on the downscaling strategy than on the model skills in simulating present-day conditions. Uncoupled downscalings of sea surface temperature (SST) changes are found to be tightly constrained by the atmospheric forcing. The incorporation of coupled air-sea interaction, by contrast, allows the regional model system to develop independently. Changes in salinity show a higher sensitivity to open lateral boundary conditions and river runoff than to coupled or uncoupled atmospheric forcings. Dependencies on the downscaling strategy for changes in SST, salinity, stratification and circulation collectively affect changes in nutrient import and biological primary production. [ABSTRACT FROM AUTHOR]

Extratropical Cyclone (EC) characteristics depend on a combination of large-scale factors and regional processes. However, the latter are considered to be poorly represented in global climate models (GCMs), partly because their resolution is too coarse. This paper describes a framework using possibilities given by regional climate models (RCMs) to gain insight into storm activity during winter over North America (NA). Recent past climate period (1981-2005) is considered to assess EC activity over NA using the NCEP regional reanalysis (NARR) as a reference, along with the European reanalysis ERA-Interim (ERAI) and two CMIP5 GCMs used to drive the Canadian Regional Climate Model—version 5 (CRCM5) and the corresponding regional-scale simulations. While ERAI and GCM simulations show basic agreement with NARR in terms of climatological storm track patterns, detailed bias analyses show that, on the one hand, ERAI presents statistically significant positive biases in terms of EC genesis and therefore occurrence while capturing their intensity fairly well. On the other hand, GCMs present large negative intensity biases in the overall NA domain and particularly over NA eastern coast. In addition, storm occurrence over the northwestern topographic regions is highly overestimated. When the CRCM5 is driven by ERAI, no significant skill deterioration arises and, more importantly, all storm characteristics near areas with marked relief and over regions with large water masses are significantly improved with respect to ERAI. Conversely, in GCM-driven simulations, the added value contributed by CRCM5 is less prominent and systematic, except over western NA areas with high topography and over the Western Atlantic coastlines where the most frequent and intense ECs are located. Despite this significant added-value on seasonal-mean characteristics, a caveat is raised on the RCM ability to handle storm temporal ‘seriality’, as a measure of their temporal variability at a given location. In fact, the driving models induce some significant footprints on the RCM skill to reproduce the intra-seasonal pattern of storm activity. [ABSTRACT FROM AUTHOR]

Using the outgoing longwave radiation (OLR) data from the National Oceanic and Atmospheric Administration archives, this paper analyzes the interdecadal variation of convective activities over the central Pacific (CP) from July to October of 1979–2013 and its impact on tropical cyclone (TC) genesis in the western North Pacific (WNP). Concurrent with the interdecadal decrease of TC genesis, the tropical convection underwent a significant interdecadal change in the late 1990s. Overall, the first leading empirical orthogonal function mode of the tropical OLR during July–October turned from a zonal dipole pattern during 1979–1997 to a tripole pattern during 1998–2013. Concomitant to this change, the boreal part of the Walker circulation shrank westward, with its downdraft branch located over the CP. The downward motion anomalies over the CP increased after the late 1990s, as did the trade easterlies. Consistent with the CP convective activity anomalies, the negative low-level relative vorticity anomalies and upper-level divergence anomalies, positive vertical wind shear anomalies and anomalous abundant water vapor can be observed over the southeastern part of the WNP. Additionally, the tropical depression (TD)-type waves associated with the CP convective activities are significantly different before and after the late 1990s. Before the late 1990s, the off-equatorial TD-type waves could be distinctly observed, with clear transitions located along the WNP monsoon trough. However, these transitions were vague after the late 1990s. Therefore, the convective activities over the CP may have played an important role in affecting the interdecadal change of TC genesis by affecting the genesis of TD-type waves. [ABSTRACT FROM AUTHOR]

The intensity of European heatwaves is connected to specific synoptic atmospheric circulation. Given the relatively small number of observations, estimates of the connection between the circulation and temperature require ad hoc statistical methods. This can be achieved through the use of analogue methods, which allow to determine a distribution of temperature conditioned to the circulation. The computation of analogues depends on a few parameters. In this article, we evaluate the influence of the variable representing the circulation, the size of the domain of computation, the length of the dataset, and the number of analogues on the reconstituted temperature anomalies. We tested the sensitivity of the reconstitution of temperature to these parameters for four emblematic recent heatwaves: June 2003, August 2003, July 2006 and July 2015. The paper provides general guidelines for the use of flow analogues to investigate European summer heatwaves. We found that Z500 is better suited than SLP to simulate temperature anomalies, and that rather small domains lead to better reconstitutions. The dataset length has an important influence on the uncertainty. We conclude by a set of recommendations for an optimal use of analogues to probe European heatwaves. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of initialised decadal hindcasts of the North Atlantic subpolar gyre (SPG) using the HiGEM model, which has a nominal grid-spacing of 90 km in the atmosphere, and 1/3∘ in the ocean. HiGEM decadal predictions (HiGEM-DP) exhibit significant skill at capturing 0–500 m ocean heat content in the SPG, and outperform historically forced transient integrations and persistence for up to a decade ahead. An analysis of case-studies of North Atlantic decadal change, including the 1960s cooling, the mid-1990s warming, and the post-2005 cooling, show that changes in ocean circulation and heat transport dominate the predictions of the SPG. However, different processes are found to dominate heat content changes in different regions of the SPG. Specifically, ocean advection dominates in the east, but surface fluxes dominate in the west. Furthermore, compared to previous studies, we find a smaller role for ocean heat transport changes due to ocean circulation anomalies at the latitudes of the SPG, and, for the 1960s cooling, a greater role for surface fluxes. Finally, HiGEM-DP predicts the observed positive state of the North Atlantic Oscillation in the early 1990s. These results support an important role for the ocean in driving past changes in the North Atlantic region, and suggest that these changes were predictable. [ABSTRACT FROM AUTHOR]

Major storm systems over Europe frequently have a tropical origin. This paper analyses the characteristics and dynamics of such cyclones in the observational record, using MERRA reanalysis data for the period 1979-2013. By stratifying the cyclones along three key phases of their development (tropical phase, extratropical transition and final re-intensification), we identify four radically different life cycles: the tropical cyclone and extratropical cyclone life cycles, the classic extratropical transition and the warm seclusion life cycle. More than 50% of the storms reaching Europe from low latitudes follow the warm seclusion life cycle. It also contains the strongest cyclones. They are characterized by a warm core and a frontal T-bone structure, with a northwestward warm conveyor belt and the effects of dry intrusion. Rapid deepening occurs in the latest phase, around their arrival in Europe. Both baroclinic instability and release of latent heat contribute to the strong intensification. The pressure minimum occurs often a day after entering Europe, which enhances the potential threat of warm seclusion storms for Europe. The impact of a future warmer climate on the development of these storms is discussed. [ABSTRACT FROM AUTHOR]

The paper uses observational data from 1950 to 2014 to investigate rapid intensification (RI) variability of tropical cyclones (TCs) in the North Atlantic and its relationships with large-scale climate variations. RI is defined as a TC intensity increase of at least 15.4 m/s (30 knots) in 24 h. The seasonal RI distribution follows the seasonal TC distribution, with the highest number in September. Although an RI event can occur anywhere over the tropical North Atlantic (TNA), there are three regions of maximum RI occurrence: (1) the western TNA of 12°N-18°N and 60°W-45°W, (2) the Gulf of Mexico and the western Caribbean Sea, and (3) the open ocean southeast and east of Florida. RI events also show a minimum value in the eastern Caribbean Sea north of South America-a place called a hurricane graveyard due to atmospheric divergence and subsidence. On longer time scales, RI displays both interannual and multidecadal variability, but RI does not show a long-term trend due to global warming. The top three climate indices showing high correlations with RI are the June-November ENSO and Atlantic warm pool indices, and the January-March North Atlantic oscillation index. It is found that variabilities of vertical wind shear and TC heat potential are important for TC RI in the hurricane main development region, whereas relative humidity at 500 hPa is the main factor responsible for TC RI in the eastern TNA. However, the large-scale oceanic and atmospheric variables analyzed in this study do not show an important role in TC RI in the Gulf of Mexico and the open ocean southeast and east of Florida. This suggests that other factors such as small-scale changes of oceanic and atmospheric variables or TC internal processes may be responsible for TC RI in these two regions. Additionally, the analyses indicate that large-scale atmospheric and oceanic variables are not critical to TC genesis and formation; however, once a tropical depression forms, large-scale climate variations play a role in TC intensification. [ABSTRACT FROM AUTHOR]

In this paper, the regional climate model REMO is used to investigate the added value of downscaling low resolutions global climate models (GCMs) and the climate change projections over Central Africa. REMO was forced by two GCMs (EC-Earth and MPI-ESM), for the period from 1950 to 2100 under the Representative Concentration Pathway 8.5 scenario. The performance of the REMO simulations for current climate is compared first with REMO simulation driven by ERA-Interim reanalysis, then by the corresponding GCMs in order to determine whether REMO outputs are able to effectively lead to added value at local scale. We found that REMO is generally able to better represent some aspects of the rainfall inter-annual variability, the daily rainfall intensity distribution as well as the intra-seasonal variability of the Central African monsoon, though few biases are still evident. It is also found that the boundary conditions strongly influences the spatial distribution of seasonal 2-m temperature and rainfall. From the analysis of the climate change signal from the present period 1976-2005 to the future 2066-2095, we found that all models project a warming at the end of the twenty-first century although the details of the climate change differ between REMO and the driving GCMs, specifically in REMO where we observe a general decrease in rainfall. This rainfall decrease is associated with delayed onset and anticipated recession of the Central African monsoon and a shortening of the rainy season. Small-scales variability of the climate change signal for 2-m temperature are usually smaller than that of the large-scales climate change part. For rainfall however, small-scales induce change of about 70% compared to the present climate statistics. [ABSTRACT FROM AUTHOR]

The statistical method, Average Predictability Time (APT) decomposition, is used in the present paper to estimate the decadal predictability of upper ocean heat content over the global ocean, North Pacific and North Atlantic, respectively. The twentieth century simulations from CMIP5 outputs are the main data sources in this study. On global scale, the leading predictable component is characterized by a warming trend over the majority of oceans, which is related to the anthropogenic forced response. The second predictable component has significant loadings in the North Atlantic, especially in the subtropical region, which originates from the Atlantic Multidecadal Oscillation (AMO) predictability. To separate interactions among different ocean basins, we further maximize APT in individual North Pacific and North Atlantic oceans. It is found that the second and the third predictable component in North Pacific are significantly correlated with the well-known North Pacific Gyre Oscillation mode and the Pacific Decadal Oscillation respectively. Upper limit prediction skill of these two components are on the order of 6 years. In contrast, the most predictable component derived from the North Atlantic features an AMO-like spatial structure with its prediction skill up to 18 years, while the basin mode due to global warming only exists as the third component. This indicates the interdecadal variability in the North Atlantic is strong enough to mask the anthropogenic climate signals. Furthermore, predictability in the real world is also investigated and compared with model results by using observation-based data. [ABSTRACT FROM AUTHOR]

Global Climate Models (GCMs) have been extensively used in many climate change impact studies. However, the coarser resolution of these GCM outputs is not adequate to assess the potential effects of climate change on local scale. Downscaling techniques have thus been proposed to resolve this problem either by dynamical or statistical approaches. The statistical downscaling (SD) methods are widely privileged because of their simplicity of implementation and use. However, many of them ignore the observed spatial dependence between different locations, which significantly affects the impact study results. An improved multi-site SD approach is thus presented in this paper to downscaling of daily precipitation at many sites concurrently. This approach is based on a combination of multiple regression models for rainfall occurrences and amounts and the Singular Value Decomposition technique, which models the stochastic components of these regression models to preserve accurately the space-time statistical properties of the daily precipitation. Furthermore, this method was able to describe adequately the intermittency property of the precipitation processes. The proposed approach has been assessed using 10 rain gauges located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the National Centers for Environmental Prediction/National Centre for Atmospheric Research re-analysis data set. The results have indicated the ability of the proposed approach to reproduce accurately multiple observed statistical properties of the precipitation occurrences and amounts, the at-site temporal persistence, the spatial dependence between sites and the temporal variability and spatial intermittency of the precipitation processes. [ABSTRACT FROM AUTHOR]

Statistical downscaling methods are commonly used to address the scale mismatch between coarse resolution Global Climate Model output and the regional or local scales required for climate change impact assessments. The effectiveness of a downscaling method can be measured against four broad criteria: consistency with the existing baseline data in terms of means, trends and distributional characteristics; consistency with the broader scale climate data used to generate the projections; the degree of transparency and repeatability; and the plausibility of results produced. Many existing downscaling methods fail to fulfil all of these criteria. In this paper we examine a block bootstrap simulation technique combined with a quantile prediction and matching method for simulating future daily climate data. By utilising this method the distributional properties of the projected data will be influenced by the distribution of the observed data, the trends in predictors derived from the Global Climate Models and the relationship of these predictors to the observed data. Using observed data from several climate stations in Vanuatu and Fiji and out-of-sample validation techniques, we show that the method is successful at projecting various climate characteristics including the variability and auto-correlation of daily temperature and rainfall, the correlations between these variables and between spatial locations. This paper also illustrates how this novel method can produce more effective point scale projections and a more credible alternative to other approaches in the Pacific region. [ABSTRACT FROM AUTHOR]

Recent winter seasons have evidenced that global warming does not exclude the occurrence of exceptionally cold and/or snowy episodes in the Northern mid-latitudes. The expected rarefaction of such events is likely to exacerbate both their societal and environmental impacts. This paper therefore aims to evaluate model uncertainties underlying the fate of wintertime cold extremes over Europe. Understanding why climate models (1) still show deficiencies in simulating present-day features and (2) differ in their responses under future scenarios for the twentyfirst century indeed constitutes a crucial challenge. Here we propose a weather-regime approach in order to separate the contributions of large-scale circulation and non-dynamical processes to biases or changes in the simulated mean and extreme temperatures. We illustrate our methodology from the wintertime occurrence of extremely cold days in idealized atmosphere-only experiments performed with two of the CMIP5 climate models (CNRM-CM5 and IPSL-CM5A-LR). First we find that most of the present-day temperature biases are due to systematic errors in non-dynamical processes, while the main features of the large-scale dynamics are well captured in such experiments driven by observed sea-surface temperatures, with the exception of a generalized underestimation of blocking episodes. Then we show that uncertainties associated with changes in large-scale circulation modulate the depletion in cold extremes under an idealized scenario for the late twentyfirst century. These preliminary results suggest that the original methodology proposed in this paper can be helpful for understanding spreads of larger model-ensembles when simulating the response of temperature extremes to climate change. [ABSTRACT FROM AUTHOR]

Spain is one of the European countries with most environmental problems related to water scarcity and droughts. Additionally, several studies suggest trends of increasing temperature and decreasing rainfall, mainly for the Iberian Peninsula, due to climate variability and change. While Regional Climate Models (RCM) are a valuable tool for understanding climate processes, the causes and plausible impacts on variables and meteorological extremes present a wide range of associated uncertainties. The multi-model ensemble approach allows the quantification and reduction of uncertainties in the predictions. The combination of models (RCM in this case), generally increases the reliability of the predictions, although there are different weighting methodologies. In this paper, a strategy is presented for the building of non-stationary PDF (probability density functions) ensembles with the aim of evaluating the spatial pattern of future risk of drought for an area. At the same time, the uncertainty associated with the metric used in the construction of the PDF ensembles is assessed. A comparative study of methodologies based on the application of the Reliability Ensemble Averaging (REA), assessing its factors using two performance measures, on the one hand the Perkins Score Methods, on the other hand the Kolmogorov-Smirnov test, is proposed. The evaluation of the sensitivity of the methodologies used in the construction of ensembles, as proposed in this paper, although without completely eliminating uncertainty, allows a better understanding of the sources and magnitude of the uncertainties involved. Despite the differences between the spatial distribution results from each metric (which can be in the order of 40 % in some areas), both approaches concluded about a plausible significant and widespread increase throughout continental Spain of the mean value of annual maximum dry spell lengths (AMDSL) between the years 1990 and 2050. Finally, the more parsimonious approach in the building of ensembles PDF, based on AMDSL in peninsular Spain, is identified. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model's ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty. [ABSTRACT FROM AUTHOR]

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution. [ABSTRACT FROM AUTHOR]

EC-Earth is a newly developed global climate system model. Its core components are the Integrated Forecast System (IFS) of the European Centre for Medium Range Weather Forecasts (ECMWF) as the atmosphere component and the Nucleus for European Modelling of the Ocean (NEMO) developed by Institute Pierre Simon Laplace (IPSL) as the ocean component. Both components are used with a horizontal resolution of roughly one degree. In this paper we describe the performance of NEMO in the coupled system by comparing model output with ocean observations. We concentrate on the surface ocean and mass transports. It appears that in general the model has a cold and fresh bias, but a much too warm Southern Ocean. While sea ice concentration and extent have realistic values, the ice tends to be too thick along the Siberian coast. Transports through important straits have realistic values, but generally are at the lower end of the range of observational estimates. Exceptions are very narrow straits (Gibraltar, Bering) which are too wide due to the limited resolution. Consequently the modelled transports through them are too high. The strength of the Atlantic meridional overturning circulation is also at the lower end of observational estimates. The interannual variability of key variables and correlations between them are realistic in size and pattern. This is especially true for the variability of surface temperature in the tropical Pacific (El Niño). Overall the ocean component of EC-Earth performs well and helps making EC-Earth a reliable climate model. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate several timely, daily air-sea heat flux products (NCEP, NCEP2, ERA-Interim and OAFlux/ISCCP) against observations and present the newly developed TropFlux product. This new product uses bias-corrected ERA-interim and ISCCP data as input parameters to compute air-sea fluxes from the COARE v3.0 algorithm. Wind speed is corrected for mesoscale gustiness. Surface net shortwave radiation is based on corrected ISCCP data. We extend the shortwave radiation time series by using 'near real-time' SWR estimated from outgoing longwave radiation. All products reproduce consistent intraseasonal surface net heat flux variations associated with the Madden-Julian Oscillation in the Indian Ocean, but display more disparate interannual heat flux variations associated with El Niño in the eastern Pacific. They also exhibit marked differences in mean values and seasonal cycle. Comparison with global tropical moored buoy array data, I-COADS and fully independent mooring data sets shows that the two NCEP products display lowest correlation to mooring turbulent fluxes and significant biases. ERA-interim data captures well temporal variability, but with significant biases. OAFlux and TropFlux perform best. All products have issues in reproducing observed longwave radiation. Shortwave flux is much better captured by ISCCP data than by any of the re-analyses. Our 'near real-time' shortwave radiation performs better than most re-analyses, but tends to underestimate variability over the cold tongues of the Atlantic and Pacific. Compared to independent mooring data, NCEP and NCEP2 net heat fluxes display ~0.78 correlation and >65 W m rms-difference, ERA-I performs better (~0.86 correlation and ~48 W m) while OAFlux and TropFlux perform best (~0.9 correlation and ~43 W m). TropFlux hence provides a useful option for studying flux variability associated with ocean-atmosphere interactions, oceanic heat budgets and climate fluctuations in the tropics. [ABSTRACT FROM AUTHOR]

In order to take into account uncertainties in the future climate projections there is a growing demand for probabilistic projections of climate change. This paper presents a methodology for producing such a probabilistic analysis of future temperature extremes. The 20- and 100-years return levels are obtained from that of the normalized variable and the changes in mean and standard deviation given by climate models for the desired future periods. Uncertainty in future change of these extremes is quantified using a multi-model ensemble and a perturbed physics ensemble. The probability density functions of future return levels are computed at a representative location from the joint probability distribution of mean and standard deviation changes given by the two combined ensembles of models. For the studied location, the 100-years return level at the end of the century is lower than 41°C with an 80% confidence. Then, as the number of model simulations is low to compute a reliable distribution, two techniques proposed in the literature (local pattern scaling and ANOVA) have been used to infer the changes in mean and standard deviation for the combinations of RCM and GCM which have not been run. The ANOVA technique leads to better results for the reconstruction of the mean changes, whereas the two methods fail to correctly infer the changes in standard deviation. As standard deviation change has a major impact on return level change, there is a need to improve the models and the different techniques regarding the variance changes. [ABSTRACT FROM AUTHOR]

This paper aims to provide a new blocking definition with applicability to observations and model simulations. An updated review of previous blocking detection indices is provided and some of their implications and caveats discussed. A novel blocking index is proposed by reconciling two traditional approaches based on anomaly and absolute flows. Blocks are considered from a complementary perspective as a signature in the anomalous height field capable of reversing the meridional jet-based height gradient in the total flow. The method succeeds in identifying 2-D persistent anomalies associated to a weather regime in the total flow with blockage of the westerlies. The new index accounts for the duration, intensity, extension, propagation, and spatial structure of a blocking event. In spite of its increased complexity, the detection efficiency of the method is improved without hampering the computational time. Furthermore, some misleading identification problems and artificial assumptions resulting from previous single blocking indices are avoided with the new approach. The characteristics of blocking for 40 years of reanalysis (1950-1989) over the Northern Hemisphere are described from the perspective of the new definition and compared to those resulting from two standard blocking indices and different critical thresholds. As compared to single approaches, the novel index shows a better agreement with reported proxies of blocking activity, namely climatological regions of simultaneous wave amplification and maximum band-pass filtered height standard deviation. An additional asset of the method is its adaptability to different data sets. As critical thresholds are specific of the data set employed, the method is useful for observations and model simulations of different resolutions, temporal lengths and time variant basic states, optimizing its value as a tool for model validation. Special attention has been paid on the devise of an objective scheme easily applicable to General Circulation Models where observational thresholds may be unsuitable due to the presence of model bias. Part II of this study deals with a specific implementation of this novel method to simulations of the ECHO-G global climate model. [ABSTRACT FROM AUTHOR]

This paper presents a comparison of principal component (PC) regression and regularized expectation maximization (RegEM) to reconstruct European summer and winter surface air temperature over the past millennium. Reconstruction is performed within a surrogate climate using the National Center for Atmospheric Research (NCAR) Climate System Model (CSM) 1.4 and the climate model ECHO-G 4, assuming different white and red noise scenarios to define the distortion of pseudoproxy series. We show how sensitivity tests lead to valuable “a priori” information that provides a basis for improving real world proxy reconstructions. Our results emphasize the need to carefully test and evaluate reconstruction techniques with respect to the temporal resolution and the spatial scale they are applied to. Furthermore, we demonstrate that uncertainties inherent to the predictand and predictor data have to be more rigorously taken into account. The comparison of the two statistical techniques, in the specific experimental setting presented here, indicates that more skilful results are achieved with RegEM as low frequency variability is better preserved. We further detect seasonal differences in reconstruction skill for the continental scale, as e.g. the target temperature average is more adequately reconstructed for summer than for winter. For the specific predictor network given in this paper, both techniques underestimate the target temperature variations to an increasing extent as more noise is added to the signal, albeit RegEM less than with PC regression. We conclude that climate field reconstruction techniques can be improved and need to be further optimized in future applications. [ABSTRACT FROM AUTHOR]

August 2006 was an exceptionally wet month in the Netherlands, in particular near the coast where rainfall amounts exceeded 300% of the climatological mean. August 2006 was preceded by an extremely warm July with a monthly mean temperature of almost 1°C higher than recorded in any other summer month in the period 1901–2006. This had resulted in very high sea surface temperatures (SSTs) in the North Sea at the end of July. In this paper the contribution of high SSTs to the high rainfall amounts is investigated. In the first part of this study, this is done by analyzing short-term integrations with a regional climate model (RACMO2) operated at 6 km resolution, which are different in the prescribed values of the SSTs. In the second part of the paper the influence of SSTs on rainfall is analyzed statistically on the basis of daily observations in the Netherlands during the period 1958–2006. The results from both the statistical analysis as well as the model integrations show a significant influence of SSTs on precipitation. This influence is particularly strong in the coastal area, that is, less than 30–50 km from the coastline. With favorable atmospheric flow conditions, the analyzed dependency is about +15% increase per degree temperature rise, thereby exceeding the Clausius–Clapeyron relation—which is often used as a temperature related constraint on changes in extreme precipitation—by approximately a factor of two. It is shown that the coastal area has consistently become wetter compared to the inland area since the 1950s. This finding is in agreement with the rather strong observed trend in SST over the same period and the dependencies of rainfall on SST reported in this study. [ABSTRACT FROM AUTHOR]

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

This paper is mainly concerned with the understanding and attribution of the recent observed freshening trend in the subpolar North Atlantic Ocean. From previous coupled model studies and an analysis of the long HadCM3 control simulation, it seems unlikely that this freshening trend is a direct consequence of anthropogenically forced climate change. It is shown in this paper that the subpolar North Atlantic can be freshened to the observed degree without invoking substantial large-scale surface freshwater flux changes. The source of freshening can come from a freshwater redistribution within the Arctic/subpolar North Atlantic. The redistribution (involving both liquid water and sea ice) is carried by a perturbed ocean circulation change in the subpolar seas and triggered by deep convection in the Labrador Sea. The freshening can be widespread but mainly in the north and northwest of the subpolar North Atlantic. A sustained 30–40 years freshening trend can be easily identified in specific locations such as the Labrador Sea or in the basin wide integral of freshwater storage. At the peak, the model subpolar North Atlantic can hold around 10,000 km3 of extra freshwater. An analysis of 1,400 years HadCM3 control simulation also reveals a good correlation between freshwater content anomalies and gyre transport in the subpolar North Atlantic on decadal timescales. A general mechanism involving circulation regime changes and freshwater redistribution between the subpolar North Atlantic and the Arctic/Nordic Seas is proposed, which can resolve a number of seemingly contradictory observed changes in the North Atlantic and contributes to the longer term goal of a full understanding of recent North Atlantic fresh water changes. [ABSTRACT FROM AUTHOR]

Various proxy data reveal that in many regions of the Northern Hemisphere (NH), the middle Holocene (6 kyr BP) was warmer than the early Holocene (8 kyr BP) as well as the later Holocene, up to the end of the pre-industrial period (1800 AD). This pattern of warming and then cooling in the NH represents the response of the climate system to changes in orbital forcing, vegetation cover and the Laurentide Ice Sheet (LIS) during the Holocene. In an attempt to better understand these changes in the climate system, the McGill Paleoclimate Model (MPM) has been coupled to the dynamic global vegetation model known as VECODE (see Part I of this two-part paper), and a number of sensitivity experiments have been performed with the “green” MPM. The model results illustrate the following: (1) the orbital forcing together with the vegetation-albedo feedback result in the gradual cooling of global SAT from about 6 kyr BP to the end of the pre-industrial period; (2) the disappearance of the LIS over the period 8-6 kyr BP, associated with vegetation-albedo feedback, allows the global SAT to increase and reach its maximum at around 6 kyr BP; (3) the northern limit of the boreal forest moves northward during the period 8-6.4 kyr BP due to the LIS retreat; (4) during the period 6.4-0 kyr BP, the northern limit of the boreal forest moves southward about 120 km in response to the decreasing summer insolation in the NH; and (5) the desertification of northern Africa during the period 8-2.6 kyr BP is mainly explained by the decreasing summer monsoon precipitation. [ABSTRACT FROM AUTHOR]

Orographic precipitation and snowpack provide a vital water resource for the western U.S., while convective precipitation accounts for a significant part of annual precipitation in the eastern U.S. As a result, water managers are keenly interested in their fate under climate change. However, previous studies of water cycle changes in the U.S. have been conducted with climate models of relatively coarse resolution, leading to potential misrepresentation of key physical processes. This paper presents results from a high-resolution climate change simulation that permits convection and resolves mesoscale orography at 4-km grid spacing over much of North America using the Weather Research and Forecasting (WRF) model. Two 13-year simulations were performed, consisting of a retrospective simulation (October 2000-September 2013) with initial and boundary conditions from ERA-interim and a future climate sensitivity simulation with modified reanalysis-derived initial and boundary conditions through adding the CMIP5 ensemble-mean high-end emission scenario climate change. The retrospective simulation is evaluated by validating against Snowpack Telemetry (SNOTEL) and an ensemble of gridded observational datasets. It shows overall good performance capturing the annual/seasonal/sub-seasonal precipitation and surface temperature climatology except for a summer dry and warm bias in the central U.S. In particular, the WRF seasonal precipitation agrees with SNOTEL observations within a few percent over the mountain ranges, providing confidence in the model's estimation of western U.S. seasonal snowfall and snowpack. The future climate simulation forced with warmer and moister perturbed boundary conditions enhances annual and winter-spring-fall seasonal precipitation over most of the contiguous United States (CONUS), but suppresses summertime precipitation in the central U.S. The WRF-downscaled climate change simulations provide a high-resolution dataset (i.e., High-Resolution CONUS downscaling, HRCONUS) to the community for studying one possible scenario of regional climate changes and impacts. [ABSTRACT FROM AUTHOR]

In the northern hemisphere, there are six permanent or semi-permanent atmospheric activity centers, namely the Icelandic Low, Aleutian Low, India Low, Mongolia High, North Pacific High, and North Atlantic High. The first four are semi-permanent action centers and the last two are permanent circulation systems. The India Low exists only during the summer. By using 160 years (1850-2009) of monthly mean sea level pressure data from the Hadley Centre in the UK, we conduct a comprehensive study of the five boreal winter atmospheric centers of action (ACAs). Based on a unified definition and a method determined in previous studies, we calculate the indices of areal coverage (S), intensity (P), and position of action center ( λ , φ ) for each of these five ACAs. Through an in-depth analysis of these indices and their relationships with climate variables, we evaluate the indices by describing and explaining areal climate anomalies, particularly precipitation and temperature anomalies in China. We show that (1) ACAs significantly influence the climate anomalies of surrounding areas (2) the influences of oceanic ACAs are larger and the intensity anomalies of ACAs have a greater impact than their location displacement, and (3) ACAs exert more control on temperature than they do on precipitation. For the two ACAs over the north Atlantic, the impacts of their intensities on the anomalies of temperature and precipitation are similar. For the two ACAs over the north Pacific, their influences are almost the opposite. The most influential ACA for climate anomalies in China during the boreal winter is HMO. When HMO is stronger, China has a colder winter and it is wetter in the north. With stronger ACAs in the upstream, i.e., the Icelandic Low and North Atlantic High, northern China has a warmer winter. The ACAs over the north Pacific exert little influence on climate anomalies in China during winter. The analyses presented in this paper provide a set of useful indices for defining and describing ACAs, and they suggest insightful applications for these indices. [ABSTRACT FROM AUTHOR]

One of the main challenges in climate change studies is accurate projection of the global warming impacts on the probabilistic behaviour of hydro-climate processes. Due to the complexity of climate-associated processes, identification of predictor variables from high dimensional atmospheric variables is considered a key factor for improvement of climate change projections in statistical downscaling approaches. For this purpose, the present paper adopts a new approach of supervised dimensionality reduction, which is called 'Supervised Principal Component Analysis (Supervised PCA)' to regression-based statistical downscaling. This method is a generalization of PCA, extracting a sequence of principal components of atmospheric variables, which have maximal dependence on the response hydro-climate variable. To capture the nonlinear variability between hydro-climatic response variables and projectors, a kernelized version of Supervised PCA is also applied for nonlinear dimensionality reduction. The effectiveness of the Supervised PCA methods in comparison with some state-of-the-art algorithms for dimensionality reduction is evaluated in relation to the statistical downscaling process of precipitation in a specific site using two soft computing nonlinear machine learning methods, Support Vector Regression and Relevance Vector Machine. The results demonstrate a significant improvement over Supervised PCA methods in terms of performance accuracy. [ABSTRACT FROM AUTHOR]

The Arctic cell as a reversed and closed loop next to the Polar cell has been recently revealed in the Northern Hemisphere (NH). In this paper, we study the interannual variability of the Arctic and Polar cell strengths during 1979-2012, and their influence on surface air temperature (SAT), precipitation, and sea-ice concentration (SIC) at mid- and high-latitudes of the NH. We show that there is a significant negative correlation between the Arctic and Polar cell strengths. Both the Arctic and Polar cell strengths can well indicate the recurring climatic anomalies of SAT, precipitation, and SIC in their extreme winters. The surface large-scale cold-warm and dry-wet anomalous patterns in these extreme winters are directly linked with the vertical structure of height and temperature anomalies in the troposphere. Results suggest that the past climatic anomalies are better indicated by the strength anomalies of the Polar and Arctic cells than the traditional indices of mid-high latitude pattern such as the Arctic Oscillation and North Atlantic Oscillation. This study illustrates a three-dimensional picture of atmospheric variable anomalies in the troposphere that result in surface climatic anomalies. [ABSTRACT FROM AUTHOR]

This paper is a pioneering analysis of past climates in southern South America combining multiproxy reconstructions and the state-of-the-art CMIP5/PMIP3 paleoclimatic models to investigate the time evolution of regional climatic conditions from the Mid-Holocene (MH) to the present. This analysis allows a comparison between the impact of the long term climate variations associated with insolation changes and the more recent effects of anthropogenic forcing on the region. The PMIP3 multimodel experiments suggest that changes in precipitation over almost all southern South America between MH and pre-industrial (PI) times due to insolation variations are significantly larger than those between PI and the present, which are due to changes in greenhouse gas concentrations. Anthropogenic forcing has been particularly intense over western Patagonia inducing reduction of precipitation in summer, autumn and winter as a consequence of progressively weaker westerly winds over the region, which have moved further poleward, between ca. 35-55°S and have become stronger south of about 50°S. Orbital variations between the MH to the PI period increased insolation over southern South America during summer and autumn inducing warmer conditions in the PI, accentuated by the effect of anthropogenic forcing during the last century. On the other hand, changes in orbital parameters from the MH to the PI period reduced insolation during winter and spring inducing colder conditions, which have been reversed by the anthropogenic forcing. [ABSTRACT FROM AUTHOR]

Hadley circulation (HC) is a planetary scale circulation spanning one-third of the globe from tropics to the sub-tropics. Recent changes in HC width and its temporal variability is a topic of paramount interest because of the climate implications it carry alongside. The present study attempts to bring out the subtropical climate change indications in the comparatively new Japanese Re-analysis (JRA55) dataset by means of the mean meridional stream function (MSF). The observed features of HC in JRA55 are found to be reproduced in NCEP, MERRA and ECMWF datasets, with notable differences in the magnitudes of MSF. The calculated annual cycle of HC edges, center and total width from this dataset closely resembles the annual cycle of the respective parameters derived from the rest of the datasets, with very less inter-annual variability. For the first time, MSF estimated using four reanalysis datasets (JRA55, NCEP, MERRA and ECMWF datasets) are verified with observations from integrated global radiosonde archive datasets, using the process of subsampling. The features so estimated show a high degree of similarity amongst each other as well as with observations. The monthly trend in the total width of the HC is quantified to show a maximum of expansion during the month of July, which is significant at the 95 % confidence interval for all datasets. The present paper also discusses the presence of a 'minor circulation' feature in the northern hemisphere which is centered on 34°N during the June and July months, but not in all years. The significance of the present study lies in evaluating the relatively new JRA55 datasets with widely used reanalysis data sets and radiosonde observations and revelation of a minor circulation not discussed hitherto in the context of HC dynamics. [ABSTRACT FROM AUTHOR]

Statistical downscaling techniques are required to refine atmosphere-ocean global climate data and provide reliable meteorological information such as a realistic temporal variability and relationships between sites and variables in a changing climate. To this end, the present paper introduces a modular structure combining two statistical tools of increasing interest during the last years: (1) Gaussian copula and (2) quantile regression. The quantile regression tool is employed to specify the entire conditional distribution of downscaled variables and to address the limitations of traditional regression-based approaches whereas the Gaussian copula is performed to describe and preserve the dependence between both variables and sites. A case study based on precipitation and maximum and minimum temperatures from the province of Quebec, Canada, is used to evaluate the performance of the proposed model. Obtained results suggest that this approach is capable of generating series with realistic correlation structures and temporal variability. Furthermore, the proposed model performed better than a classical multisite multivariate statistical downscaling model for most evaluation criteria. [ABSTRACT FROM AUTHOR]

Sub-daily rainfall extremes are of significant societal interest, with implications for flash flooding and the design of urban stormwater systems. It is increasingly recognised that extreme subdaily rainfall will intensify as a result of global temperature increases, with regional climate models (RCMs) representing one of the principal lines of evidence on the likely magnitude and spatiotemporal characteristics of these changes. To evaluate the ability of RCMs to simulate subdaily extremes, it is common to compare the simulated statistical characteristics of the extreme rainfall events with those from observational records. While such analyses are important, they provide insufficient insight into whether the RCM reproduces the correct underlying physical processes; in other words, whether the model 'gets the right answers for the right reasons'. This paper develops a range of metrics to assess the performance of RCMs in capturing the physical mechanisms that produce extreme rainfall. These metrics include the diurnal and seasonal cycles, relationship between rainfall intensity and temperature, temporal scaling, and the spatial structure of extreme rainfall events. We evaluate a high resolution RCM-the Weather Research Forecasting model-over the Greater Sydney region, using three alternative parametrization schemes. The model shows consistency with the observations for most of the proposed metrics. Where differences exist, these are dependent on both the rainfall duration and model parameterization strategy. The use of physically meaningful performance metrics not only enhances the confidence in model simulations, but also provides better diagnostic power to assist with future model improvement. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s00382-021-05756-2 [ABSTRACT FROM AUTHOR]

In the context of ongoing global warming, understanding the East Asian Summer Monsoon (EASM) dynamics is of great significance for predicting future climate changes. This study reconstructed the mid-Holocene precipitation variations on the basis of pollen data obtained from Taojiacun section in the Luoyang Basin within the Central Plains of China. Our research results show that the region experienced three dry intervals during the data-covering period (~ 8.08 to ~ 4.76 cal. kyr BP): ~8.08 to ~ 7.35, ~ 6.42 to ~ 5.59, and ~ 4.94 to ~ 4.76 cal. kyr BP. The intervened were two wet intervals: ~7.35 to ~ 6.42 and ~ 5.59 to ~ 4.94 cal. kyr BP. The chronological correspondence between drier (wetter) intervals in the Luoyang Basin and warmer (cooler) intervals in the western Tropical Pacific implies that the north-south shifts of the Western Pacific Subtropical High (WPSH), driven by the western Tropical Pacific Sea Surface Temperature (SST), regulated the positions of the EASM-related rain-belt during the data-covering period. We compared the mean annual precipitation (Pann) sequence from Taojiacun section with those from the southern and northern neighboring areas. With a full consideration of chronological uncertainties and sampling resolution differences, it can be generalized that those drier (wetter) intervals in the Luoyang Basin within the Central Plains of China were broadly correspondent with those drier (wetter) intervals expressed in the sequences of the southern neighboring areas (i.e., the middle and lower reaches of the Yangtze River with an addition of the Huai River Basin). It means that the changes in precipitation in the Central Plains was most likely coincident with that in the southern neighboring areas during the data-covering period (~ 8.08 to ~ 4.76 cal. kyr BP). [ABSTRACT FROM AUTHOR]

The Three-River Headwater Region (TRHR) has undergone significant hydrological changes and affects the water resources security in the local and downstream areas. We use multiple observational precipitation product, as well as the ERA5 reanalysis dataset to investigate the precipitation variations over the TRHR during the rainy season from 1990 to 2020, and to analyze the contributions of local evaporation and remote water vapor transport to the precipitation and its variations. The precipitation shows a significant increasing trend (17.8 mm decade−1). The precipitation variability (PV) is larger at daily scale and smaller at interannual scale. The spatial distribution of PV varies on different timescales, which reflects the different dominant regimes driving the moisture transport. The mean precipitation in the TRHR is predominantly contributed by western and southern moisture influxes, and the increasing trend of these influxes (10.8 kg s−1 decade−1) determines the precipitation increase. Local evaporation provide about 6% of water vapor for the precipitation, while it has little influence on the precipitation change. The anomalous precipitation in wet and dry years is mainly controlled by the atmospheric circulation over the Tibetan Plateau, which regulates the southwestern moisture transport towards the TRHR. Additionally, the precipitation during 1990–2020 increases by about 3.9% compared to that during 1960–1989. It is related to the higher conversion ratio of water vapor influxes to precipitation and larger evaporation over the TRHR. These findings improve our understanding of the precipitation variations in the TRHR and provide insights for policy makers to optimize water resources management to cope with the global climate change. [ABSTRACT FROM AUTHOR]

In this study, using the NCAR Community Earth System Model (CESM1.2.2), we investigate the changes in the characteristics of the summer monsoon low pressure systems (LPS) over India in a twenty-first century climate change simulation corresponding to the RCP8.5 scenario. A slight weakening in monsoon circulation and an increase in mean summer monsoon precipitation over India are simulated in a warmer climate, consistent with several previous studies. The weakening of the monsoon circulation is associated with a pair of anticyclonic anomalies straddling the equator in the low level, weakening the cross-equatorial monsoonal flow from the southern hemisphere. These low-level circulation anomalies appear to be robust features in the equatorial Indian Ocean under climate change. An increase in moisture flux is also simulated over the Indian subcontinent. However, we find no significant change in the number of LPS or their spatial distribution under the RCP8.5 scenario. This is attributed to a small but non-significant decrease in the low-level meridional cyclonic shear in zonal winds. An increase in the intensity and frequency of extreme precipitation over India in a warmer world is simulated and is likely associated with an increase in moisture content. Our study shows that the fractional contribution of LPS to mean and extreme precipitation over India in a warmer world is likely unchanged, but the frequency and intensity of extreme events are larger. Because of the diversity in the results from single model studies on LPS characteristics in a warmer world, a future study that uses CMIP6 multi-model data would be valuable to assess the robustness and the uncertainty in changes in LPS activities under climate change. [ABSTRACT FROM AUTHOR]

In this paper, the spring predictability barrier (SPB) problem for two types of El Niño events is investigated. This is enabled by tracing the evolution of a conditional nonlinear optimal perturbation (CNOP) that acts as the initial error with the biggest negative effect on the El Niño predictions. We show that the CNOP-type errors for central Pacific-El Niño (CP-El Niño) events can be classified into two types: the first are CP-type-1 errors possessing a sea surface temperature anomaly (SSTA) pattern with negative anomalies in the equatorial central western Pacific, positive anomalies in the equatorial eastern Pacific, and accompanied by a thermocline depth anomaly pattern with positive anomalies along the equator. The second are, CP-type-2 errors presenting an SSTA pattern in the central eastern equatorial Pacific, with a dipole structure of negative anomalies in the east and positive anomalies in the west, and a thermocline depth anomaly pattern with a slight deepening along the equator. CP-type-1 errors grow in a manner similar to an eastern Pacific-El Niño (EP-El Niño) event and grow significantly during boreal spring, leading to a significant SPB for the CP-El Niño. CP-type-2 errors initially present as a process similar to a La Niña-like decay, prior to transitioning into a growth phase of an EP-El Niño-like event; but they fail to cause a SPB. For the EP-El Niño events, the CNOP-type errors are also classified into two types: EP-type-1 errors and 2 errors. The former is similar to a CP-type-1 error, while the latter presents with an almost opposite pattern. Both EP-type-1 and 2 errors yield a significant SPB for EP-El Niño events. For both CP- and EP-El Niño, their CNOP-type errors that cause a prominent SPB are concentrated in the central and eastern tropical Pacific. This may indicate that the prediction uncertainties of both types of El Niño events are sensitive to the initial errors in this region. The region may represent a common sensitive area for the targeted observation of the two types of El Niño events. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is used to simulate daily minimum temperature time series coming from observations and two CMIP5 climate models (IPSL-CM5A-MR and CNRM-CM5) in order to analyze the changes in cold wave number and proportions under future climate conditions. The stochastic model allows computing 100 temperature time series for each different source (observation or climate model), and for 22 locations in France, which enables inferring the statistical significance of the changes. Two future time periods, near (around 2010-2060) and far future (around 2050-2100), and two RCPs (RCP4.5 and RCP8.5) are considered, while 3 different thresholds are used to identify cold waves: 0 °C and the 10th and 5th percentiles of observed wintertime (December-January-February) daily minimum temperature distribution. The results show that both models project a significantly lower number of cold waves in the future, all durations considered, but the changes mainly concern the proportion of the longest cold waves (10 days and more). The decreases are higher with IPSL-CM5A-MR than with CNRM-CM5. The main driver of this change is the decreasing frequency of the observation based thresholds in the future, which is higher for IPSL-CM5-MR model because the impact of a higher mean is enhanced by a decrease in the variance. [ABSTRACT FROM AUTHOR]

In this paper, using the fully coupled NCAR Community Earth System Model (CESM1.0.4), we investigate the relative importance of CO-fertilization, climate warming, anthropogenic nitrogen deposition, and land use and land cover change (LULCC) for terrestrial carbon uptake during the historical period (1850-2005). In our simulations, between the beginning and end of this period, we find an increase in global net primary productivity (NPP) on land of about 4 PgCyr (8.2 %) with a contribution of 2.3 PgCyr from CO-fertilization and 2.0 PgCyr from nitrogen deposition. Climate warming also causes NPP to increase by 0.35 PgCyr but LULCC causes a decline of 0.7 PgCyr. These results indicate that the recent increase in vegetation productivity is most likely driven by CO fertilization and nitrogen deposition. Further, we find that this configuration of CESM projects that the global terrestrial ecosystem has been a net source of carbon during 1850-2005 (release of 45.1 ± 2.4 PgC), largely driven by historical LULCC related CO fluxes to the atmosphere. During the recent three decades (early 1970s to early 2000s), however, our model simulations project that the terrestrial ecosystem acts as a sink, taking up about 10 PgC mainly due to CO fertilization and nitrogen deposition. Our results are in good qualitative agreement with recent studies that indicate an increase in vegetation production and water use efficiency in the satellite era and that the terrestrial ecosystem has been a net sink for carbon in recent decades. [ABSTRACT FROM AUTHOR]

Quantifying the urbanization effect on trends in climate extremes is important both for detection and attribution studies and for human adaptation; however, a fundamental problem is how to accurately estimate a trend and its statistical significance, especially for non-Gaussian and serially dependent data. In this paper, the choice of trend estimation and significance testing method is suggested as important for these kinds of studies, as illustrated by quantifying the urbanization effect on trends in seven hot-extreme indices for the megacity of Shanghai during 1961-2013. Both linear and nonlinear trend estimation methods were used. The trends and corresponding statistical significances were estimated by taking into account potential non-Gaussian and serial dependence in the extreme indices. A new method based on adaptive surrogate data is proposed to test the statistical significance of the ensemble empirical mode decomposition (EEMD) nonlinear trend. The urbanization contribution was found to be approximately 34 % (43 %) for the trend in the non-Gaussian distributed heat wave index based on nonparametric linear trend (EEMD nonlinear trend) estimation. For some of the other six hot-extreme indices analyzed, the urbanization contributions estimated based on linear and nonlinear trends varied greatly, with as much as a twofold difference between them. For the linear trend estimation itself, the ordinary least squares fit can give a substantially biased estimation of the urbanization contribution for some of the non-Gaussian extreme indices. [ABSTRACT FROM AUTHOR]

Seasonal prediction of the East Asian summer monsoon (EASM) strength is probably one of the most challenging and crucial issues for climate prediction over East Asia. In this paper, a statistical method called partial-least square (PLS) regression is utilized to uncover principal sea surface temperature (SST) modes in the winter preceding the EASM. Results show that the SST pattern of the first PLS mode is associated with the turnabout of warming (or cooling) phase of a mega-El Niño/Southern Oscillation (mega-ENSO) (a leading mode of interannual-to-interdecadal variations of global SST), whereas that of the second PLS mode leads the warming/cooling mega-ENSO by about 1 year, signaling precursory conditions for mega-ENSO. These indicate that mega-ENSO may provide a critical predictability source for the EASM strength. Based on a 40-year training period (1958-1997), a PLS prediction model is constructed using the two leading PLS modes and 3-month-lead hindcasts are performed for the validation period of 1998-2013. A promising skill is obtained, which is comparable to the ensemble mean of versions 3 and 4 of the Canadian Community Atmosphere Model (CanCM3/4) hindcasts from the newly developed North American Multi-model Ensemble Prediction System regarding the interannual variations of the EASM strength. How to improve dynamical model simulation of the EASM is also examined through comparing the CanCM3/4 hindcast (1982-2010) with the 106-year historical run (1900-2005) by the Second Generation Canadian Earth System Model (CanESM2). CanCM3/4 exhibits a high skill in the EASM hindcast period 1982-2010 during which it also has a better performance in capturing the relationship between the EASM and mega-ENSO. By contrast, the simulation skill of CanESM2 is quite low and it is unable to reproduce the linkage between the EASM and mega-ENSO. All these results emphasize importance of mega-ENSO in seasonal prediction and dynamical model simulation of the EASM. [ABSTRACT FROM AUTHOR]

This paper documents the capability of the ARW/WRF regional climate model to regionalize near-surface atmospheric variables at high resolution (8 km) over Burgundy (northeastern France) from daily to interannual timescales. To that purpose, a 20-year continuous simulation (1989-2008) was carried out. The WRF model driven by ERA-Interim reanalyses was compared to in situ observations and a mesoscale atmospheric analyses system (SAFRAN) for five near-surface variables: precipitation, air temperature, wind speed, relative humidity and solar radiation, the last four variables being used for the calculation of potential evapotranspiration (ET). Results show a significant improvement upon ERA-Interim. This is due to a good skill of the model to reproduce the spatial distribution for all weather variables, in spite of a slight over-estimation of precipitation amounts mostly during the summer convective season, and wind speed during winter. As compared to the Météo-France observations, WRF also improves upon SAFRAN analyses, which partly fail at showing realistic spatial distributions for wind speed, relative humidity and solar radiation-the latter being strongly underestimated. The SAFRAN ET is thus highly under-estimated too. WRF ET is in better agreement with observations In order to evaluate WRF's capability to simulate a reliable ET, the water balance of thirty Douglas-fir stands was computed using a process-based model. Three soil water deficit indexes corresponding to the sum of the daily deviations between the relative extractible water and a critical value of 40 % below which the low soil water content affects tree growth, were calculated using the nearest weather station, SAFRAN analyses weather data, or by merging observation and WRF weather variables. Correlations between Douglas-fir growth and the three estimated soil water deficit indexes show similar results. These results showed through the ET estimation and the relation between mean annual SWDI and Douglas-fir growth index that the main difficulties of the WRF model to simulate soil water deficit is mainly attributable to its precipitation biases. In contrast, the low discrepancies between WRF and observations for air temperature, wind speed, relative humidity and solar radiation make then usable for the water balance and ET computation. [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]