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This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian region. [ABSTRACT FROM AUTHOR]

Research into weather circulation changes over the UK for future climate has mainly focused on changes in the Summer and Winter seasons, with less analysis on seasonality and the transition seasons. Using the 30 Met Office weather patterns we examine the influence of climate change on seasonality through atmospheric circulation using a number of climate models. Changes in seasonality are important as they can have large impacts on many sectors including agriculture, energy and tourism. This paper finds a noticeable increase in Autumn over the UK in the frequency of drier summer-type regimes and a decrease in stormy winter types that emerge as early as the 2020s. The change in circulation signal once isolated from the overall signal is responsible for a 4–12% decrease in Autumn mean rainfall on average for England by the end of this century (where the values in the range are dependent on the emissions scenario). This change is projected over English regions that are already experiencing water stress, and with predictions of drier summers over the UK in future, this could further increase drought risk. The change in circulation in Autumn also moderates the large increase in the number of large-scale extreme daily rainfall events over the same regions predicted due to climate change. While this future circulation change is replicated across all the climate models used, large differences remain in the strength of the signal between models. The climate models used replicate the frequency of the 30 weather patterns well for all seasons. [ABSTRACT FROM AUTHOR]

A number of recent papers have examined sea level data, both local tide gauge records and regional/global averages, to estimate not only how fast sea level is rising but how the rate has changed over time, i.e. its pattern of acceleration and deceleration. In addition, a number of claims of cyclic/quasi-periodic variations have been proposed. However, many of these papers contain technical problems which call their results into question. In particular, the issue of autocorrelation is often ignored, and even when it is addressed its impact has sometimes been misinterpreted. Autocorrelation does more than just affect the standard errors of regression analysis, it can also make the spectrum of a noise process distinctly 'red' and therefore be highly suggestive of low-frequency periodic or pseudo-periodic behavior when none is present. If any analysis is applied which acts as a band-pass filter, it can further exaggerate the illusion of oscillatory behavior. These issues are highlighted in a small number of recent papers, in order to improve the quality of future work on this subject. [ABSTRACT FROM AUTHOR]

Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming. [ABSTRACT FROM AUTHOR]

This paper presents the first multi-model ensemble of 10-year, "convection-permitting" kilometer-scale regional climate model (RCM) scenario simulations downscaled from selected CMIP5 GCM projections for historical and end of century time slices. The technique is to first downscale the CMIP5 GCM projections to an intermediate 12–15 km resolution grid using RCMs, and then use these fields to downscale further to the kilometer scale. The aim of the paper is to provide an overview of the representation of the precipitation characteristics and their projected changes over the greater Alpine domain within a Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study and the European Climate Prediction system project, tasked with investigating convective processes at the kilometer scale. An ensemble of 12 simulations performed by different research groups around Europe is analyzed. The simulations are evaluated through comparison with high resolution observations while the complementary ensemble of 12 km resolution driving models is used as a benchmark to evaluate the added value of the convection-permitting ensemble. The results show that the kilometer-scale ensemble is able to improve the representation of fine scale details of mean daily, wet-day/hour frequency, wet-day/hour intensity and heavy precipitation on a seasonal scale, reducing uncertainty over some regions. It also improves the representation of the summer diurnal cycle, showing more realistic onset and peak of convection. The kilometer-scale ensemble refines and enhances the projected patterns of change from the coarser resolution simulations and even modifies the sign of the precipitation intensity change and heavy precipitation over some regions. The convection permitting simulations also show larger changes for all indices over the diurnal cycle, also suggesting a change in the duration of convection over some regions. A larger positive change of frequency of heavy to severe precipitation is found. The results are encouraging towards the use of convection-permitting model ensembles to produce robust assessments of the local impacts of future climate change. [ABSTRACT FROM AUTHOR]

Projections of West African Monsoon (WAM) precipitation are uncertain. To address this, an improved understanding of the mechanisms driving WAM precipitation change is needed to shed light on inter-model differences and aid model development. The full forcing of increased CO2 can be decomposed into different components such as the impact of ocean warming, or the direct radiative effect of increased CO2. This paper investigates such a decomposition, analysing the effect of a uniform 4K ocean warming whilst keeping atmospheric CO2 concentrations constant. The analysis highlights several mechanisms acting to decrease WAM precipitation over a range of timescales, from days after the abrupt ocean warming, to the long-term equilibrium response. The initial decrease in WAM precipitation is caused by warming and enhanced convection over the ocean, stabilising the atmosphere inland and disrupting the monsoon inflow at low levels. Later in the response (after about 5 days), the WAM precipitation is reduced through a strengthening of the shallow circulation over West Africa, associated with changes in the large-scale temperature gradients and a local warming of the atmosphere related to a soil moisture feedback mechanism over the Sahel. Finally, from around 20 days after the SST increase, the WAM precipitation is also reduced through changes in specific humidity gradients that lead to increased potency of dry air advection into the monsoon rainband. The analysis concludes by demonstrating that the processes affecting precipitation in the early stages of the response are also relevant to the long-term equilibrium response. [ABSTRACT FROM AUTHOR]

Quantifying the extent of change in climatic and hydrological variables in the past and the future is essential for climate change-resilient development, especially in the climate change sensitive region of Nepal. This paper analyzed future climatic trends and extremes, historical hydrological extremes and their linkage with historical precipitation extremes, and discussed the impacts of climate change on various sectors in the less-studied Small and Medium-sized Eastern River Basins (SMERB) of Nepal. Applying an ensemble of five Coupled Model Intercomparison Project phase 6 (CMIP6) global climate models (GCMs), we evaluated 10 precipitation and 13 temperature extreme indices using Climpact2 for the historical (1979–2020), near-future (2021–2045), mid-future (2046–2070) and far-future (2071–2100) under two Shared Socioeconomic Pathway (SSP), SSP245 and SSP585. Hydrological extreme indices were assessed using Indicators of Hydrologic Alteration tool. Projected climate revealed significant increase (9–73%) in annual and seasonal rainfall except winter; frequent intense rainfall extremes but prolonged dry spells; significant increasing minimum and maximum temperature trends (0.4–5 °C); rise in extreme heat events; increasing minimum and maximum discharge extreme trends in most hydrological stations; as well as strong association between maximum 1 day precipitation (Rx1day) and 1 day max flow for all stations. Growing dry periods but intense rainfall in few wet days, coupled with warming pattern all-over SMERB with frequent extreme events indicate high risk for future climate-related disasters. The harsher climate will potentially have damaging implications, especially in climate-induced disasters, food security, and water and sanitation infrastructure. [ABSTRACT FROM AUTHOR]

Regional climate projections indicate that European summer precipitation may change considerably in the future. Southern Europe can expect substantial drying while Northern Europe could actually become wetter. Model spread and internal variability in these projections are large, however, and unravelling the processes that underlie the changes is essential to get more confidence in these projections. Large-scale circulation change is one of the contributors to model spread. In this paper we quantify the role of future large-scale circulation changes to summer precipitation change, using a 16-member single-model ensemble obtained with the regional climate model RACMO2, forced by the global climate model EC-Earth2.3 and the RCP8.5 emission scenario. Using the method of circulation analogues three contributions to the future precipitation change are distinguished. The first is the precipitation change occurring without circulation change (referred to as the thermodynamic term). This contribution is characterised by a marked drying-to-wetting gradient as one moves north from the Mediterranean. The second contribution measures the effects of changes in the mean circulation. It has a very different spatial pattern and is closely related to the development of a region of high pressure (attaining its maximum west of Ireland) and the associated anti-cyclonic circulation response. For a large area east of Ireland including parts of western Europe, it is the major contributor to the overall drying signal, locally explaining more than 90% of the ensemble-mean change. In regions where the patterns overlap, the signal-to-noise ratio of the total change is either enhanced or reduced depending on their relative signs. Although the second term is expected to be particularly model dependent, the high-pressure region west of Ireland also appears in CMIP5 and CMIP6 ensemble-mean projections. The third contribution records the effects of changes in the circulation variability. This term has the smallest net contribution, but a relatively large uncertainty. The analogues are very good in partitioning the ensemble-mean precipitation change, but describe only up to 40% of the ensemble-spread. This demonstrates that other precipitation-drivers (SST, spring soil moisture etc.) will generally strongly influence trends in single climate realisations. This also re-emphasises the need for large ensembles or using alternative methods like the Pseudo Global Warming approach where signal to noise ratios are higher. Nevertheless, identifying the change mechanisms helps to understand the future uncertainties and differences between models. [ABSTRACT FROM AUTHOR]

Changing climate has altered the trends and variability of precipitation and temperature globally and thereby increasing the risk of natural and social disasters, especially in coastal climatic transitional zones such as the Huaihe river basin (HRB). This paper applies the Empirical Quantile Mapping (EQM) method for bias correction and systematically evaluates the performance of the 30 Global climate models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating precipitation and temperature over the Huaihe river basin (HRB) for 1979–2014. A suitable multi-model ensemble subset (BMME) is selected based on the Euclidean Distance (ED) synthetic metric. Results show that in comparison to the baseline period 1995–2014, precipitation (temperature) over HRB is projected to increase at the rate of around 15 mm/decade (0.1 °C/decade), 16 mm/decade (0.3 °C/decade), 20 mm/decade (0.5 °C/decade), and 15 mm/decade (0.6 °C/decade) for the period 2015–2100, under Shared Socioeconomic Pathways SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. In the long-term (2081–2100), the annual precipitation is projected to increase by 32%, 27%, 35%, and 26%, under the four scenarios, respectively. The temperature is projected to remain stable or slightly decrease under SSP1-2.6 (2 °C) and SSP2-4.5 (3 °C), while will keep rising and increasing by 6 °C under SSP5-8.5 and by 4 °C under SSP3-7.0 by 2100. The isotherm and isohyet are projected to shift northwest from southeastern coastal China to inland in the future, which is likely associated with the northwestward shift of the western Pacific anticyclone in summer. [ABSTRACT FROM AUTHOR]

For those involved in planning for regional and local scale changes in future climate, there is a requirement for climate information to be available in a context more usually associated with meteorological timescales. Here we combine a tool used in numerical weather prediction, the 30 weather patterns produced by the Met Office, which are already applied operationally to numerical weather prediction models, to assess changes in the UK Climate Projections (UKCP) Global ensemble. Through assessing projected changes in the frequency of the weather patterns at the end of the 21st Century, we determine that future changes in large-scale circulation tend towards an increase in winter of weather patterns associated with cyclonic and westerly wind conditions at the expense of more anticyclonic, settled/blocked weather patterns. In summer, the results indicate a shift towards an increase in dry settled weather types with a corresponding reduction in the wet and windy weather types. Climatologically this suggests a shift towards warmer, wetter winters and warmer, drier summers; which is consistent with the headline findings from the UK Climate Projections 2018. This paper represents the first evaluation of weather patterns analysis within UKCP Global. It provides a detailed assessment of the changes in these weather patterns through the 21st Century and how uncertainty in emissions, structural and perturbed parameters affects these results. We show that the use of these weather patterns in tandem with the UKCP projections is useful for future work investigating changes in a range of weather-related climate features such as extreme precipitation. [ABSTRACT FROM AUTHOR]

Dynamical downscaling (DD) consists in using archives of Coupled Global Climate Models (CGCM) simulations as the atmospheric and sea-surface boundary conditions (BC) to drive nested, Regional Climate Model (RCM) simulations. Biases in the CGCM-generated driving BC, however, can have detrimental impacts on RCM performance. It is well documented for the historical period that CGCM-simulated sea-surface temperatures (SST) suffer substantial biases, especially important near coastal regions. Assuming that these SST biases are time-invariant, they could in principle be subtracted from century-long CGCM projections before being used to drive RCMs. This paper investigates the performance of a 3-step DD approach as follows. The CGCM-simulated sea-surface temperatures (SST) are first empirically corrected by subtracting their systematic biases; the corrected SST are then used as ocean surface BC for an atmosphere-only GCM (AGCM) simulation; finally this AGCM simulation provides the atmospheric lateral BC to drive an RCM simulation. This is what we refer to as the 3-step approach CGCM–AGCM–RCM of DD, which can be compared to the traditional 2-step approach CGCM–RCM consisting of driving an RCM simulation directly by CGCM-generated BC. In this paper we compare the results obtained with the two approaches, for present and future climates under RCP8.5, using the fifth-generation Canadian Regional Climate Model (CRCM5) with a grid mesh of 0.22° over the North American CORDEX domain, driven by two CMIP5 models: the Canadian Earth System Model of the Canadian Centre for Climate modelling and analysis (CanESM2) and the Earth System Model of the Max-Planck-Institut für Meteorologie (MPI-ESM-MR). The results show that, in current climate, the seasonal-mean 2-m temperature fields simulated with the 3-step DD have generally smaller biases with respect to the observations than those simulated with the 2-step DD; in fact the performance of the 3-step DD simulations often approaches that of the reanalyses-driven simulation. For the seasonal-mean precipitation field, however, the differences between the two DD methods are not conclusive. Differences between the projected climate changes with the two DD methods vary substantially depending upon the variable being considered. Differences are particularly important for temperature: over the bulk of the North American continent, the 3-step DD projects more warming in winter and less in summer. This result highlights the nonlinearities of the climate system, and constitutes an additional measure of uncertainty with DD. [ABSTRACT FROM AUTHOR]

Analysis of wind generated waves due to the prevailing climate change induced by global warming is vital for benefit to the coastal community. In the present paper, the hindcasting of decadal variation namely (i) 1979–1992, (ii) 1993–2006, and (iii) 2007–2019 of wind-wave climate over seasonal and annual scale through 41 years starting from 1979 is carried out using ERA5 reanalysis wind-forcing with a daily temporal and 0.5° × 0.5° spatial resolution. The numerical simulations are executed over the computational domains (i) Global—Indian Ocean and (ii) Regional—south Gujarat to north Goa for evaluating the variability analysis of annual and seasonal mean significant wave heights. The evaluation framework is validated over three in-situ measurement locations with good correlation as compared to the simulated results. The results indicate that at this site the wind and wave activity is noted to increase during transition from 1979–1992 to 1993–2006, then a declination of mean significant wave height is noted during the span 2007–2019. Further, along with the spatial variation estimation, 27 locations along 15 m bathymetric contour at 25 km interval are selected for trend line study. All the 27 locations exhibits a similar kind of trend by variation of decreasing trend from 1979–2019 with variations 0.07–0.22 cm/year. This study highlights the significance in simulating the prevailing wind-wave changes at a regional level considering the impact of the climatology and seasonality changes for effective coastal management, environmental impact assessments and sustainable development. [ABSTRACT FROM AUTHOR]

In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale 'perfect' reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of "black-box" models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs. [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]

10.1007/s00382-020-0539-w 21 Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P. Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. All these issue are currently under discussion within the CORDEX community, so that the CORDEX-CORE initiative can remain a central resource for the climate modeling and climate service communities. The CORDEX-CORE EXP-I dataset is indeed intended to be a community resource for the study of regional climate phenomena in a climate change context and for application to vulnerability-impacts-adaptation (VIA) assessment studies and climate service activities. The Coordinated Regional Downscaling Experiment (CORDEX, Giorgi et al. [15]; Jones et al. [24]) is the main reference program for the climate downscaling community. [Extracted from the article]

Optimal fingerprinting is a standard method for detecting climate changes. Among the uncertainties taken into account by this method, one is the fact that the response to climate forcing is not known exactly, but in practice is estimated from ensemble averages of model simulations. This uncertainty can be taken into account using an Error-in-Variables model (or equivalently, the Total Least Squares method), and can be expressed through confidence intervals. Unfortunately, the predominant paradigm (likelihood ratio theory) for deriving confidence intervals is not guaranteed to work because the number of parameters that are estimated in the Error-in-Variables model grows with the number of observations. This paper discusses various methods for deriving confidence intervals and shows that the widely-used intervals proposed in the seminal paper by Allen and Stott are effectively equivalent to bias-corrected intervals from likelihood ratio theory. A new, computationally simpler, method for computing these intervals is derived. Nevertheless, these confidence intervals are incorrect in the "weak-signal regime". This conclusion does not necessarily invalidate previous detection and attribution studies because many such studies lie in the strong-signal regime, for which standard methods give correct confidence intervals. A new diagnostic is introduced to check whether or not a data set lies in the weak-signal regime. Finally, and most importantly, a bootstrap method is shown to give correct confidence intervals in both strong- and weak-signal regimes, and always produces finite confidence intervals, in contrast to the likelihood ratio method which can give unbounded intervals that do not match the actual uncertainty. [ABSTRACT FROM AUTHOR]

The paper examines the relationships between ENSO events and tropical cyclones (TCs) in the western North Pacific (WNP) during the ENSO decaying phase of March to July [MAMJJ(1)]. It is shown that WNP TC response is asymmetric to ENSO warm and cold events during MAMJJ(1), associated with the asymmetric patterns of oceanic and atmospheric environmental variables. Eastern Pacific warm events correspond to a westward shift in TC genesis location and a significant decrease of the TC number in the southwest subregion of the WNP, which is consistent with unfavorable atmospheric and oceanic conditions for TC activity during MAMJJ(1). Correspondingly, there is a much less WNP TC number, a shorter TC duration, and a decreased tendency in all three primary TC tracks. During MAMJJ(1) of eastern Pacific cold events, an increased TC activity in the northeast subregion results in an enhanced TC genesis number in the WNP. Central Pacific ENSO events have little effect on WNP TC activity during MAMJJ(1), except that central Pacific warm events are unfavorable for TCs to form in the northwest subregion associated with atmospheric and oceanic conditions. The paper also compares and discusses the TC response to ENSO events during the ENSO developing year of March-July. [ABSTRACT FROM AUTHOR]

The Choco low-level jet is among the main regional circulation mechanisms related to the advection of water vapor from the eastern Pacific to northwestern South America. Variations in the intensity of position of the jet core are identified as determinant for regional moisture transport and associated rainfall. This paper analyzes the annual cycle of intensity and latitudinal location of this jet according to different reanalysis and observational datasets. Moreover, we compare possible changes in the Choco jet occurred during past climates, like the little ice age (LIA), with those associated with future scenarios of greenhouse gas concentrations (RCP8.5), using simulations from the Paleoclimate Modelling Intercomparison Project Phase 3 (PMIP3) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). Our results suggest that according to reanalysis/observational data, as well as the CMIP5 models with the best representation of the Choco jet in present climate, there is a positive correlation between the jet intensity and its latitudinal location, and such relationship is associated with the sea level pressure (SLP) difference between the eastern tropical Pacific and the northwestern South American landmass. Hence, stronger (weaker) SLP differences favor a stronger (weaker) intensity and a northward (southward) location of the Choco jet. PMIP3 simulations suggest a stronger and northward Choco jet during LIA due to a stronger SLP difference in comparison to present climate. However, under the RCP8.5 scenario, there is not robust agreement among CMIP5 models although the best models suggest a southward jet at the end of the 21st century. This suggests that the mechanisms influencing the Choco jet may play different roles during past natural climate changes with respect to anthropogenically-forced climate changes. [ABSTRACT FROM AUTHOR]

Previous studies in the literature show that the annual cycle of surface air temperature (SAT) is changing in both amplitude and phase, and the SAT departures from the annual cycle are long-term correlated. However, the classical definition of temperature anomalies is based on the assumption that the annual cycle is constant, which contradicts the fact of changing annual cycle. How to quantify the impact of the changing annual cycle on the long-term correlation of temperature anomaly variability still remains open. In this paper, a recently developed data adaptive analysis tool, the nonlinear mode decomposition (NMD), is used to extract and remove time-varying annual cycle to reach the new defined temperature anomalies in which time-dependent amplitude of annual cycle has been considered. By means of detrended fluctuation analysis, the impact induced by inter-annual variability from the time-dependent amplitude of annual cycle has been quantified on the estimation of long-term correlation of long historical temperature anomalies in Europe. The results show that the classical climatology annual cycle is supposed to lack inter-annual fluctuation which will lead to a maximum artificial deviation centering around 600 days. This maximum artificial deviation is crucial to defining the scaling range and estimating the long-term persistence exponent accurately. Selecting different scaling range could lead to an overestimation or underestimation of the long-term persistence exponent. By using NMD method to extract the inter-annual fluctuations of annual cycle, this artificial crossover can be weakened to extend a wider scaling range with fewer uncertainties. [ABSTRACT FROM AUTHOR]

The meteorological information found within ships’ logbooks is a unique and fascinating source of data for historical climatology. This study uses wind observations from logbooks covering the period 1815 to 1854 to reconstruct an index of El Niño Southern Oscillation (ENSO) for boreal winter (DJF). Statistically-based reconstructions of the Southern Oscillation Index (SOI) are obtained using two methods: principal component regression (PCR) and composite-plus-scale (CPS). Calibration and validation are carried out over the modern period 1979–2014, assessing the relationship between re-gridded seasonal ERA-Interim reanalysis wind data and the instrumental SOI. The reconstruction skill of both the PCR and CPS methods is found to be high with reduction of error skill scores of 0.80 and 0.75, respectively. The relationships derived during the fitting period are then applied to the logbook wind data to reconstruct the historical SOI. We develop a new method to assess the sensitivity of the reconstructions to using a limited number of observations per season and find that the CPS method performs better than PCR with a limited number of observations. A difference in the distribution of wind force terms used by British and Dutch ships is found, and its impact on the reconstruction assessed. The logbook reconstructions agree well with a previous SOI reconstructed from Jakarta rain day counts, 1830–1850, adding robustness to our reconstructions. Comparisons to additional documentary and proxy data sources are provided in a companion paper. [ABSTRACT FROM AUTHOR]

Europe and in particular its southern part are expected to undergo serious climate changes during summer in response to anthropogenic forcing, with large surface warming and decrease in precipitation. Yet, serious uncertainties remain, especially over central and western Europe. Several mechanisms have been suggested to be important in that context but their relative importance and possible interplays are still not well understood. In this paper, the role of soil-atmosphere interactions, cloud-temperature interactions and land-sea warming contrast in summer European climate change and how they interact are analyzed. Models for which evapotranspiration is strongly limited by soil moisture in the present climate are found to tend to simulate larger future decrease in evapotranspiration. Models characterized by stronger present-day anti-correlation between cloud cover and temperature over land tend to simulate larger future decrease in cloud cover. Large model-to-model differences regarding land-sea warming contrast and its impacts are also found. Warming over land is expected to be larger than warming over sea, leading to a decrease in continental relative humidity and precipitation because of the discrepancy between the change in atmospheric moisture capacity over land and the change in specific humidity. Yet, it is not true for all the models over our domain of interest. Models in which evapotranspiration is not limited by soil moisture and with a weak present-day anti-correlation between cloud cover and temperature tend to simulate smaller land surface warming. In these models, change in specific humidity over land is therefore able to match the continental increase in moisture capacity, which leads to virtually no change in continental relative humidity and smaller precipitation change. Because of the physical links that exist between the response to anthropogenic forcing of important impact-related climate variables and the way some mechanisms are simulated in the context of present-day variability, this study suggests some potentially useful metrics to reduce summer European climate change uncertainties. [ABSTRACT FROM AUTHOR]

This paper examines the uncertainty in the change in the heat content in the ocean component of a general circulation model. We describe the design and implementation of our statistical methodology. Using an ensemble of model runs and an emulator, we produce an estimate of the full probability distribution function (PDF) for the change in upper ocean heat in an Atmosphere/Ocean General Circulation Model, the Community Climate System Model v. 3, across a multi-dimensional input space. We show how the emulator of the GCM's heat content change and hence, the PDF, can be validated and how implausible outcomes from the emulator can be identified when compared to observational estimates of the metric. In addition, the paper describes how the emulator outcomes and related uncertainty information might inform estimates of the same metric from a multi-model Coupled Model Intercomparison Project phase 3 ensemble. We illustrate how to (1) construct an ensemble based on experiment design methods, (2) construct and evaluate an emulator for a particular metric of a complex model, (3) validate the emulator using observational estimates and explore the input space with respect to implausible outcomes and (4) contribute to the understanding of uncertainties within a multi-model ensemble. Finally, we estimate the most likely value for heat content change and its uncertainty for the model, with respect to both observations and the uncertainty in the value for the input parameters. [ABSTRACT FROM AUTHOR]

The incidence of major storm surges in the last decade have dramatically emphasized the immense destructive capabilities of extreme water level events, particularly when driven by severe tropical cyclones. Given this risk, it is vitally important that the exceedance probabilities of extreme water levels are accurately evaluated to inform risk-based flood and erosion management, engineering and for future land-use planning and to ensure the risk of catastrophic structural failures due to under-design or expensive wastes due to over-design are minimised. Australia has a long history of coastal flooding from tropical cyclones. Using a novel integration of two modeling techniques, this paper provides the first estimates of present day extreme water level exceedance probabilities around the whole coastline of Australia, and the first estimates that combine the influence of astronomical tides, storm surges generated by both extra-tropical and tropical cyclones, and seasonal and inter-annual variations in mean sea level. Initially, an analysis of tide gauge records has been used to assess the characteristics of tropical cyclone-induced surges around Australia. However, given the dearth (temporal and spatial) of information around much of the coastline, and therefore the inability of these gauge records to adequately describe the regional climatology, an observationally based stochastic tropical cyclone model has been developed to synthetically extend the tropical cyclone record to 10,000 years. Wind and pressure fields derived for these synthetically generated events have then been used to drive a hydrodynamic model of the Australian continental shelf region with annual maximum water levels extracted to estimate exceedance probabilities around the coastline. To validate this methodology, selected historic storm surge events have been simulated and resultant storm surges compared with gauge records. Tropical cyclone induced exceedance probabilities have been combined with estimates derived from a 61-year water level hindcast described in a companion paper to give a single estimate of present day extreme water level probabilities around the whole coastline of Australia. Results of this work are freely available to coastal engineers, managers and researchers via a web-based tool (). The described methodology could be applied to other regions of the world, like the US east coast, that are subject to both extra-tropical and tropical cyclones. [ABSTRACT FROM AUTHOR]

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas. [ABSTRACT FROM AUTHOR]

The rate of warming of the Arctic surface temperature has exceeded that of the global surface temperature in recent decades. However, the underlying process and causes of the long-term warming remain uncertain. In this paper, we explored the factors underlying variation in Arctic mean surface temperature anomalies (AMTA) using a piecewise linear model for 1920–2018. This analysis indicated that the change in AMTA during the study period could be divided into three segments, with AMTA increasing from 1920 to 1938, declining from 1939 to 1976, and finally increasing rapidly after 1977. By a newly developed rigorous formalism of information flow, we found a one-way causality from the driving forces to AMTA. Moreover, the AMTA evolution could mainly be attributed to a combined effect of anthropogenic and natural factors (e.g., CO2, aerosol, and PDO). During the first warming stage (1920–1938), the PDO and aerosols are the main factors determining the change in AMTA. During the second warming stage (1977–2018), greenhouse gases, dominated by CO2, are the major factors accounting for the Arctic warming. In 1939–1976, the observed cooling may be associated with aerosols, clouds, and land use. A better understanding of the driving mechanism underlying AMTA evolution provides insight into the historical Arctic climate change, and can improve the prediction of future changes in AMTA. [ABSTRACT FROM AUTHOR]

How soil moisture affects precipitation is an important question-with far reaching consequences, from weather prediction to centennial climate change-, albeit a poorly understood one. In this paper, an analysis of soil moisture-precipitation interactions over France based on observations is presented. A first objective of this paper is to investigate how large scale circulation modulates soil moisture-precipitation interactions, thanks to a weather regime approach. A second objective is to study the influence of soil moisture not only on precipitation but also on the difference between precipitation and evapotranspiration. Indeed, to have a total positive soil moisture-precipitation feedback, the potential decrease in precipitation associated with drier soils should be larger than the decrease in evapotranspiration that drier soils may also cause. A potential limited impact of soil moisture on precipitation is found for some weather regimes, but its sign depends on large scale circulation. Indeed, antecedent dry soil conditions tend to lead to smaller precipitation for the negative phase of the North Atlantic Oscillation (NAO) regime but to larger precipitation for the Atlantic Low regime. This differential response of precipitation to soil moisture anomalies depending on large scale circulation is traced back to different responses of atmospheric stability. For all circulation regimes, dry soils tend to increase the lifted condensation level, which is unfavorable to precipitation. But for the negative phase of the NAO, low soil moisture tends to lead to an increase of atmospheric stability while it tends to lead to a decrease of stability for Atlantic Low. Even if the impact of soil moisture anomalies varies depending on large scale circulation (it is larger for Atlantic low and the positive phase of the NAO), dry soils always lead to a decrease in evapotranspiration. As the absolute effect of antecedent soil moisture on evapotranspiration is always much larger than its effects on precipitation, for all circulation regimes dry soil anomalies subsequently lead to positive precipitation minus evapotranspiration anomalies i.e. the total soil moisture feedback is found to be negative. This negative feedback is stronger for the Atlantic Low and the positive phase of the NAO regimes. [ABSTRACT FROM AUTHOR]

Climate change projections are commonly based on multi-model ensembles of climate simulations. In this paper we consider the choice of bias models in Bayesian multimodel predictions. Buser et al. (Clim Res 44(2-3):227-241, 2010a) introduced a hybrid bias model which combines commonly used constant bias and constant relation bias assumptions. The hybrid model includes a weighting parameter which balances these bias models. In this study, we use a cross-validation approach to study which bias model or bias parameter leads to, in a specific sense, optimal climate change projections. The analysis is carried out for summer and winter season means of 2 m-temperatures spatially averaged over the IPCC SREX regions, using 19 model runs from the CMIP5 data set. The cross-validation approach is applied to calculate optimal bias parameters (in the specific sense) for projecting the temperature change from the control period (1961-2005) to the scenario period (2046-2090). The results are compared to the results of the Buser et al. (Clim Res 44(2-3):227-241, 2010a) method which includes the bias parameter as one of the unknown parameters to be estimated from the data. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to elucidate a potential use of the large samples of seasonal means that hindcasts provide for investigating different aspects of climate variability. This use of hindcasts complements their traditional uses in bias correction, real-time forecast calibration, and prediction skill assessment. For seasonal hindcast data from NCEP CFSv2 we show that a sample size 5208 for each target season is achievable. To demonstrate the utility of the proposed concept, we use this large sample dataset to illustrate how it could be used in documenting spatial variability in various moments of seasonal mean precipitation PDF over the US, and further, quantify nuances in the variations in precipitation PDF at different geographical locations with the amplitude of ENSO SSTs. It is our hope that analysis presented in this paper will accelerate utilization of seasonal hindcast datasets in furthering our understanding of different aspects of climate variability. With the advantage of the large sample size, we demonstrated that the precipitation PDF at the each grid of the CONUS can be represented by gamma distribution for a more concise and effective way to summarize precipitation variability. The availability of the large sample dataset also allowed us to analyze the statistical characteristic of the precipitation responses to the different amplitudes of ENSO SSTs. The results show that for strong warm events, enhancement in precipitation has larger amplitude than decrease in precipitation for cold events in the regions of Southern California and southeastern US. The variation of the precipitation signal over the other sub-regions including the southwestern US, mid-northwest, and mid-east shows more linear relationship with the ENSO SSTs. In response to anomalous ENSO SSTs, although the PDF of December-January-February seasonal mean precipitation anomaly is shifted from its climatological PDF, there is still a large overlap between precipitation PDFs for ENSO and its climatological counterpart. This uncertainty in seasonal mean outcomes of precipitation, therefore, limits the seasonal prediction skill. [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]

Atmospheric mid-latitude circulation is dominated by a zonal, westerly flow. Such a flow is generally symmetric, but it can be occasionally broken up by blocking anticyclones. The subsequent asymmetric flow can persist for several days. In this paper, we apply new mathematical tools based on the computation of an extremal index in order to reexamine the dynamical mechanisms responsible for the transitions between zonal and blocked flows. We discard the claim that mid-latitude circulation features two distinct stable equilibria or chaotic regimes, in favor of a simpler mechanism that is well understood in dynamical systems theory: we identify the blocked flow as an unstable fixed point (or saddle point) of a single basin chaotic attractor, dominated by the westerlies regime. We also analyze the North Atlantic Oscillation and the Arctic Oscillation atmospheric indices, whose behavior is often associated with the transition between the two circulation regimes, and investigate analogies and differences with the bidimensional blocking indices. We find that the Arctic Oscillation index, which can be thought as a proxy for a hemispheric average of the Tibaldi-Molteni blocking index, tracks unstable fixed points. On the other hand, the North Atlantic Oscillation, representative only for local properties of the North Atlantic blocking dynamics, does not show any trace of the presence of unstable fixed points of the dynamics. [ABSTRACT FROM AUTHOR]

In this paper, decadal variability of the Pacific-South America (PSA) mode is examined from year 1871 to 2008 based on the newly developed ocean and atmosphere reanalysis products. The PSA mode, mirroring the Pacific-North America mode in the Northern Hemisphere, emerges as the second EOF mode of 500 mb geopotential height anomalies. The mode displays substantial interannual-decadal variability with distinct timescales between 3-8 and 10-18 years, respectively. The decadal variability of the PSA mode is found to be associated with the coupled ocean-atmosphere interaction over the subtropical South and tropical Pacific. The subduction of the subtropical temperature anomalies in the South Pacific in conjunction with the tropical-subtropical atmospheric teleconnection plays important role in the decadal variability of the PSA mode. [ABSTRACT FROM AUTHOR]

Most of the annual rainfall over India occurs during the Southwest (June-September) and Northeast (October-December) monsoon periods. In March 2008, however, Southern peninsular India and Sri Lanka received the largest rainfall anomaly on record since 1979, with amplitude comparable to summer-monsoon interannual anomalies. This anomalous rainfall appeared to be modulated at intraseasonal timescale by the Madden Julian Oscillation, and was synchronous with a decaying La Niña event in the Pacific Ocean. Was this a coincidence or indicative of a teleconnection pattern? In this paper, we explore factors controlling rainfall over southern India and Sri Lanka between January and April, i.e. outside of the southwest and northeast monsoons. This period accounts for 20% of annual precipitation over Sri Lanka and 10% over the southern Indian states of Kerala and Tamil Nadu. Interannual variability is strong (about 40% of the January-April climatology). Intraseasonal rainfall anomalies over southern India and Sri Lanka are significantly associated with equatorial eastward propagation, characteristic of the Madden Julian Oscillation. At the interannual timescale, we find a clear connection with El Niño-Southern Oscillation (ENSO); with El Niños being associated with decreased rainfall (correlation of −0.46 significant at the 98% level). There is also a significant link with local SST anomalies over the Indian Ocean, and in particular with the inter-hemispheric sea surface temperature (SST) gradient over the Indian Ocean (with colder SST south of the equator being conducive to more rainfall, correlation of 0.55 significant at the 99% level). La Niñas/cold SSTs south of the equator tend to have a larger impact than El Niños. We discuss two possible mechanisms that could explain these statistical relationships: (1) subsidence over southern India remotely forced by Pacific SST anomalies; (2) impact of ENSO-forced regional Indian Ocean SST anomalies on convection. However, the length of the observational record does not allow distinguishing between these two mechanisms in a statistically significant manner. [ABSTRACT FROM AUTHOR]

The formulation of a new land surface scheme (LSS) with vegetation dynamics for coupling to the McGill Paleoclimate Model (MPM) is presented. This LSS has the following notable improvements over the old version: (1) parameterization of deciduous and evergreen trees by using the model’s climatology and the output of the dynamic global vegetation model, VECODE (Brovkin et al. in Ecological Modelling 101:251-261 (1997), Global Biogeochemical Cycles 16(4):1139, (2002)); (2) parameterization of tree leaf budburst and leaf drop by using the model’s climatology; (3) parameterization of the seasonal cycle of the grass leaf area index; (4) parameterization of the seasonal cycle of tree leaf area index by using the time-dependent growth of the leaves; (5) calculation of land surface albedo by using vegetation-related parameters, snow depth and the model’s climatology. The results show considerable improvement of the model’s simulation of the present-day climate as compared with that simulated in the original physically-based MPM. In particular, the strong seasonality of terrestrial vegetation and the associated land surface albedo variations are in good agreement with several satellite observations of these quantities. The application of this new version of the MPM (the “green” MPM) to Holocene millennial-scale climate changes is described in a companion paper, Part II. [ABSTRACT FROM AUTHOR]

This study explores climate-change influences on future air pollution-relevant meteorological variables (e.g., temperature, wind, humidity, boundary layer heights) and atmospheric phenomena (e.g., heat wave, marine air penetration, droughts) over California by the 2050s. The Community Earth System Model simulation results from Coupled Model Intercomparison Project Phase 5 under an emission scenario that most closely aligns with California's climate change goals were bias-corrected with respect to North American Regional Reanalysis data to reduce biases in both the climatological mean and inter-annual variations. The bias-corrected ~ 1° × 1° meteorological fields were dynamically downscaled to a resolution of 4 km × 4 km over California using the Weather Research and Forecasting model. This study focuses on summertime results, while the analysis of wintertime results will be presented in a separate paper. Our downscaled results projected a future increase of approximately 1 K in summer mean surface temperature over California under this single future climate realization. The temperature increase is larger in the nighttime than in the daytime. Water vapor mixing ratio is also projected to increase over California and off the coast. There are discernable decreases in boundary layer heights over the mountain ranges surrounding the central valley of California, while increases in boundary layer heights are observed over other regions in California. The number and duration of heat wave events are projected to increase substantially over the most populated parts of the State. The occurrence of marine air penetration events over the northern California is also projected to increase in the future. [ABSTRACT FROM AUTHOR]

Based on a single-model 100-member ensemble of climate change simulations performed with the MPI-ESM, this paper investigates how the probability density function (PDF) of atmospheric general circulation (AGC)—a marginal distribution of the full climate PDF—responds to increasing CO 2 concentration. The investigation provides a first assessment of CO 2 -induced changes in AGC-PDF derived from ensemble statistics (as oppose to time statistics in usual practice) that arise solely from internal variability consistent with the imposed CO 2 -forcing. By focusing on the first two moments of the AGC-PDF related to the Hadley circulation and the subtropical jets, we find that the major CO 2 -induced changes in the PDF are related to structure changes in the mean circulation and in the circulation variability. As CO 2 concentration increases, different components of the AGC can co-evolve with changing forcing. In particular, the mean Hadley cell co-evolves with the mean subtropical jets. The co-evolution is characterized by the concurrent Hadley cell widening and poleward shifting of the subtropical jets (particularly strong in January), by a strengthening of both the Hadley cell and the subtropical jets in October, and by a weakening of the Hadley cell and a strengthening of the northern and southern jets in January. The changes in circulation variability are more subtle, and reveal only large and spatially coherent changes in places, where the variability is already strong without increasing CO 2 concentration, such as at the northern and southern flanks of the summer southern subtropical jet, and at the northern flank of the southern polar night jet. There are some significant cross-variabilities between different components of the AGC. Overall, the CO 2 -induced AGC-changes can be described by linear trends in the first and second moments, with the trends in the means being much stronger than those in the variances and co-variances. [ABSTRACT FROM AUTHOR]

Present study attempts to project extreme precipitation indices over 34 different meteorological subdivisions and six homogeneous regions such as Northwest, Central Northeast, Northeast, West Central, Peninsular India and Hilly Region during summer monsoon season in the twenty-first century. For this purpose, the Regional Climate Model version4 (RegCM4) had been run at 50 km horizontal resolution forced with the global model GFDL-ESM2 M, during reference period 1976–2005 for the model validation, and the mid- (2031–2060) and far-future (2070–2099) for projections under RCP8.5 scenario over the South Asia CORDEX domain. In this paper, model simulated precipitation has been validated against IMD, APHRODITE and NCEP/NCAR data sets. The results indicate that RegCM4 captures the important features of seasonal precipitation and various extreme indices over the study area. The RegCM4 has projected an increase in the mean seasonal precipitation by 0.56 mm/day whereas in case of GFDL model the rate is 0.39 mm/day during the far-future relative to the reference period. The heavy precipitation indices are projected to increase more frequently (0.264/decade) than the mean precipitation rate (0.01/decade) over India. The correlations between the extreme precipitation indices and the seasonal mean precipitation are found to be strong. In addition, the consecutive dry days are projected to occur more frequently (3–5 days) over West Central (Telangana, Vidarbha and Marathwada) and west Rajasthan while consecutive wet days are projected to decrease over larger parts of India during far-future. Similarly, 1 day maximum precipitation and the simple daily intensity index are projected to increase consistently from mid- to far- futures over some sub-divisions of West coast, Hilly and Northeast regions. From a spatial probability perspective, model projection indicates more frequent severe drought and flood conditions over India. [ABSTRACT FROM AUTHOR]

The potential impacts of floods are of significant concern to our modern society raising the need to identify and quantify all the uncertainties that can impact their simulations. Climate simulations at finer spatial resolutions are expected to bring more confidence in these hydrological simulations. However, the impact of the increasing spatial resolutions of climate simulations on floods simulations has to be evaluated. To address this issue, this paper assesses the sensitivity of summer–fall flood simulations to the Canadian Regional Climate Model (CRCM) grid resolution. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim reanalysis at 0.44°, 0.22° and 0.11° resolutions are analysed at a daily time step for the 1981–2010 period. Raw CRCM5 precipitation and temperature outputs are used as inputs in the simple lumped conceptual hydrological model MOHYSE to simulate streamflows over 50 Quebec (Canada) basins. Summer–fall flooding is analysed by estimating four flood indicators: the 2-year, 5-year, 10-year and 20-year return periods from the CRCM5-driven streamflows. The results show systematic impacts of spatial resolution on CRCM5 outputs and seasonal flood simulations. Floods simulated with coarser climate datasets present smaller peak discharges than those simulated with the finer climate outputs. Smaller catchments show larger sensitivity to spatial resolution as more detail can be obtained from the finer grids. Overall, this work contributes to understanding the sensitivity of streamflow modelling to the climate model's resolution, highlighting yet another uncertainty source to consider in hydrological climate change impact studies. [ABSTRACT FROM AUTHOR]

Climatic change is exhibiting significant effects on the ecosystem of the Tibetan Plateau (TP), a climate-sensitive area. In particularly, winter frost, freezing events and snow avalanche frequently causing severe effects on ecosystem and social economy, however, few long-term winter temperature records or reconstructions hinder a better understanding on variations in winter temperature in the vast area of the TP. In this paper, we present a minimum winter (November–February) temperature reconstruction for the past 668 years based on a tree-ring network (12 new tree-ring chronologies) on the southeastern TP. The reconstruction exhibits decadal to inter-decadal temperature variability, with cold periods occurring in 1423–1508, 1592–1651, 1729–1768, 1798–1847, 1892–1927, and 1958–1981, and warm periods in 1340–1422, 1509–1570, 1652–1728, 1769–1797, 1848–1891, 1928–1957, and 1982–2007. As suggested by the comparisons with existing winter temperature series and spatial correlations with Climatic Research Unit gridded data, our reconstruction is reliable and indicative, and it can represent large-scale winter temperature variability on the southeastern TP. Furthermore, it shows an overall agreement with winter temperature from the northeastern TP on decadal to inter-decadal timescales. It also shows the possible effects of volcanic eruption and reducing solar activity on the winter temperature variability for the past six centuries on the southeastern TP. [ABSTRACT FROM AUTHOR]

The usefulness of a set of climate change projections largely depends on how well it spans a range of outcomes consistent with known uncertainties. Here, we present exploratory work towards developing a strategy to select variants of a state-of-the-art but expensive climate model suitable for climate projection studies. The strategy combines information from a set of relatively cheap, idealized perturbed parameter ensemble (PPE) and CMIP5 multi-model ensemble (MME) experiments, and uses two criteria as the basis to select model variants for a PPE suitable for future projections: (a) acceptable model performance at two different timescales, and (b) maintaining diversity in model response to climate change. This second part of a pair of papers builds upon Part I in which we established a strong relationship between model errors at weather and climate timescales across a PPE for a variety of key variables. This relationship is used to filter out parts of parameter space that do not give credible simulations of present day climate, while minimizing the impact on ranges in forcings and feedbacks that drive model responses to climate change. We use statistical emulation to explore the parameter space thoroughly, and demonstrate that about 90% can be filtered out without affecting diversity in global-scale climate change responses. This leads to the identification of plausible parts of parameter space from which model variants can be selected for projection studies. We selected and ran 50 variants from the plausible parameter combinations and validated the emulator predictions. Comparisons with the CMIP5 MME demonstrate that our approach can produce a set of plausible model variants that span a relatively wide range in model response to climate change. We also highlight how the prior expert-specified ranges for uncertain model parameters are constrained as a result of our methodology, and discuss recommendations for future work. [ABSTRACT FROM AUTHOR]

This paper aims at identifying oceanic regions outside the tropical Pacific, which may influence the El Niño Southern Oscillation (ENSO) through interannual modulation of equatorial Pacific winds. An Atmospheric General Circulation Model (AGCM) 7-members ensemble experiment forced by climatological sea surface temperature (hereafter, SST) in the tropical Pacific Ocean and observed interannually varying SST elsewhere produces ensemble-mean equatorial zonal wind stress interannual anomalies (ZWSA) over the equatorial Pacific. These ZWSA are largest during boreal winter in the western Pacific, and induce a ~0.5 °C response in the central Pacific during the following spring in a simple ocean model, that weakly but significantly correlates with the following ENSO peak amplitude. When correlated with global SST, the residual western equatorial Pacific ZWSA yield SST patterns that are reminiscent of ENSO teleconnections in the Indian, North and South Pacific, and Atlantic Oceans. We further design 20-members ensemble sensitivity experiments forced by typical SST patterns of the main climate modes for each of these regions, in order to identify regions that influence equatorial Pacific ZWSA most. In our experiments, only the Indian Ocean Basin-wide SST warming in late boreal winter produces a statistically significant ZWSA in the western equatorial Pacific, resulting in a weak but significant ~0.35 °C SST response in the central Pacific (i.e. ~35 % of the observed standard deviation) during the following spring, the season when the Bjerkness coupled feedback is particularly efficient. This paper hence agrees with previous studies, which suggest that ENSO-induced basin-wide SST signals in the Indian Ocean may contribute to the phase transition of ENSO. Our results suggest that studies exploring external influences on ENSO should adopt a global approach rather than focus on a specific region. Designing coupled model simulations would also allow investigating air-sea interactions-mediated teleconnection mechanisms, which we can't reproduce in our forced AGCM framework. [ABSTRACT FROM AUTHOR]

The Andes Cordillera plays a role in driving moisture and heat from tropical onto subtropical South America. It forces the development of a lee-side trough that covers most of western Argentina and a low-level jet that maximizes over Paraguay, eastern Bolivia and northern Argentina and is tightly linked to precipitation variability over much of central and southeastern South America. Its steep slopes and the large zonal gradients in topography between the Equator and 40°S are misrepresented in climate simulations using Global Climate Models (GCM) with resolutions coarser than about 100 km, since they naturally have a poor representation of the Andes and related circulation features. This paper analyses the impact of varying artificially the altitude of the Andes Cordillera in a GCM as well as increasing the horizontal resolution to study how these variations determine moisture fluxes and precipitation over selected regions of South America. Results show that the height of the Andes is crucial in shaping moisture fluxes pathways onto subtropical South America all year long. In particular, the low-level jet is only simulated when the Andes heights are doubled. At the same time, the relationship between the Andes shape and the location of the Bolivian High in summer is also discussed. In terms of precipitation, the lowest bias in the simulations is achieved when the horizontal resolution is increased, while in particular near the Andes foothills the simulated annual rainfall is largely determined by the Mountains shape. [ABSTRACT FROM AUTHOR]

Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata approach, long term projections with climate change impacts are performed using statistical downscaling and urban impacts are simulated with a dynamic regional model for limited number of years covering different precipitation characteristics. The evaluation of the statistical downscaling methodology over historical time period reveals large underestimation of the extreme rainfall, which is improved effectively by applying another regression model, for extreme days. The limited runs of dynamic downscaling models with different stages of urbanization for Mumbai, India, reveal spatially non uniform changes in precipitation, occurring primarily at the higher quantiles. The statistical and dynamical outputs are further integrated using quantile transformation for precipitation projection in Mumbai during 2050s. The projections show dominant impacts of urbanization compared to those from large scale changing patterns. The uniqueness of this computationally efficient framework lies in an integration of global and local factors for precipitation projections through a conjugal statistical–dynamical approach. [ABSTRACT FROM AUTHOR]

A comprehensive performance assessment of the empirical-statistical downscaling (ESD) technique named EPISODES is presented. Pertaining evaluation analyses consist of multifarious validation experiments as well as various comparisons of EPISODES' projections with those of three RCMs and two ESD methods based on the same GCM scenarios driven by two distinct representative concentration pathways (RCPs). EPISODES combines the downscaling of GCM simulations with a follow-up production of synthetic local time series. EPISODES is a comparably simple, computationally rather inexpensive technique, providing multi-variable and multi-site data that are suitable for being merged in an ensemble of RCM projections. This allows (e.g. for different RCPs) the compilation of large multi member ensembles derived from various GCM simulations via both main downscaling strategies (ESD and RCMs). Evaluation experiments reveal satisfying degrees of compliance between various results generated by EPISODES and observations. The grid cell bias for yearly values, for instance, is mostly less than 0.1∘C for temperature and 10% for precipitation totals. Recorded temperature values and precipitation totals corresponding to their 1st and the 99th percentiles are well represented by EPISODES too. Comparisons of various climate change signals derived by EPISODES and other downscaling approaches, present high levels of agreement as well. Many more findings referring to evaluation experiments and climate change projections are to be found in the paper as well as throughout the "Appendix". [ABSTRACT FROM AUTHOR]

Based on our previous study (Yu et al., Clim Dyn 49:1139-1156, 2017), this paper further investigates the interdecadal change in the relationship between the Kuroshio Extension (KE; 27°-37°N, 140°-158°E) and the East Asian summer monsoon (EASM) in the late 1980s. The summer KE sea surface temperature anomalies (SSTAs) show a significant positive relationship with the EASM over the period 1968-1987 (P1), but a significant negative connection with the EASM between 1991 and 2010 (P2). This interdecadal change in the KE-EASM relationship can be interpreted by considering the difference in the relationships of summer KE SSTAs with the East Asian subtropical westerly jet (EASWJ) and western North Pacific subtropical high (WNPSH) during the two periods. During P1, summertime KE SST warming is significantly related to the strengthened EASWJ and WNPSH, but it has close relationships with the weakened and northward-moving EASWJ and WNPSH during P2. These anomalous EASWJ and WNPSH associated with the summertime KE SST warming in P1 (P2) then favors increased (reduced) rainfall over the Yangtze River Valley that corresponds to a strong (weak) EASM, thereby leading to the significant positive (negative) KE-EASM relationship during this period. This change in the relationships of summer KE SSTAs with the EASWJ and WNPSH may be attributed to the increased KE SST variability associated with an enhanced Pacific Decadal Oscillation (PDO) in summer during P2, which is most probably induced by the stronger North Pacific Oscillation (NPO)-like atmospheric forcing, especially its southern pole (SP), in the preceding spring during this period. The spring NPO-like SP forces the KE SSTAs and PDO more directly during the following summer and can thus have been a better precursor for the following EASM than the full NPO-like dipole after the late 1980s. [ABSTRACT FROM AUTHOR]

The present paper uses the satellite era data from 1979 to 2015 to examine the relationship between El Niño-Southern Oscillation (ENSO) and tropical cyclones (TCs) in the western North Pacific (WNP) during the boreal summer from June to August. It is found that WNP TC variability is characterized by two major feature changes: (1) a significant reduction of the TC number since 1998 and (2) a stronger interannual relationship between ENSO and TCs since 1998. Results suggested that such changes are largely due to the synergy effects of a shifting ENSO and the Pacific climate regime shift. Since 1998 with a cool Pacific decadal oscillation phase switching from a warm phase, more La Niña and central Pacific (CP) El Niño events occur. The decreased low-level relative vorticity and increased vertical wind shear during 1998-2015 compared to 1979-1997 are responsible for the TC reduction. The stronger interannual relationship between ENSO and TCs since 1998 is closely associated the change of CP sea surface temperature. It enhances the associations of environmental factors including vertical wind shear and mid-level relative humidity with TCs and thus increases the interannual relationship between ENSO and TCs. These two feature changes also manifest in the mean TC genesis location, with a northwestward shift of the TC genesis location during 1998-2015 and an increased relation to El Niño Modoki index since 1998. This study has an important implication for TC outlooks in the WNP based on climate predictions and projections. [ABSTRACT FROM AUTHOR]

Despite decades of efforts and improvements in the representation of processes as well as in model resolution, current global climate models still suffer from a set of important, systematic biases in sea surface temperature (SST), not much different from the previous generation of climate models. Many studies have looked at errors in the wind field, cloud representation or oceanic upwelling in coupled models to explain the SST errors. In this paper we highlight the relationship between latent heat flux (LH) biases in forced atmospheric simulations and the SST biases models develop in coupled mode, at the scale of the entire intertropical domain. By analyzing 22 pairs of forced atmospheric and coupled ocean-atmosphere simulations from the CMIP5 database, we show a systematic, negative correlation between the spatial patterns of these two biases. This link between forced and coupled bias patterns is also confirmed by two sets of dedicated sensitivity experiments with the IPSL-CM5A-LR model. The analysis of the sources of the atmospheric LH bias pattern reveals that the near-surface wind speed bias dominates the zonal structure of the LH bias and that the near-surface relative humidity dominates the east-west contrasts. [ABSTRACT FROM AUTHOR]

This paper provides a detailed description of how ENSO events affect seasonal and extreme precipitation over Indonesia. Daily precipitation data from 97 stations across Indonesia covering the period from 1981 to 2012 were used to investigate the effects of El Niño and La Niña on extreme precipitation characteristics including intensity, frequency and duration, as defined based on a subset of the Expert Team on Climate Change Detection and Indices (ETCCDI). Although anomalous signals in these three indices were consistent with those of total rainfall, anomalies in the duration of extremes [i.e., consecutive dry days (CDD) and consecutive wet days (CWD)] were much more robust. El Niño impacts were particularly prominent during June-July-August (JJA) and September-October-November (SON), when anomalously dry conditions were experienced throughout the country. However, from SON, a wet anomaly appeared over northern Sumatra, later expanding eastward during December-January-February (DJF) and March-April-May (MAM), creating contrasting conditions of wet in the west and dry in the east. We attribute this apparent eastward expansion of a wet anomaly during El Niño progression to the equatorial convergence of two anti-cyclonic circulations, one residing north of the equator and the other south of the equator. These anti-cyclonic circulations strengthen and weaken according to seasonal changes and their coupling with regional seas, hence shaping moisture transport and convergence. During La Niña events, the eastward expansion of an opposite (i.e., dry) anomaly was also present but less prominent than that of El Niño. We attribute this to differences in regional ocean—atmosphere coupling, which result in the contrasting seasonal evolution of the two corresponding anomalous cyclonic circulations and in turn suggests the strong nonlinearity of El Niño and La Niña responses over the Maritime Continent. Based on the seasonal behaviour of anomalous CDD and CWD, we propose five sub-divisions of the Indonesian region for both El Niño and La Niña. [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of projected future climate changes over China to the horizontal resolution of a regional climate model RegCM4.4 (RegCM), using RCP8.5 as an example. Model validation shows that RegCM performs better in reproducing the spatial distribution and magnitude of present-day temperature, precipitation and climate extremes than the driving global climate model HadGEM2-ES (HadGEM, at 1.875° × 1.25° degree resolution), but little difference is found between the simulations at 50 and 25 km resolutions. Comparison with observational data at different resolutions confirmed the added value of the RCM and finer model resolutions in better capturing the probability distribution of precipitation. However, HadGEM and RegCM at both resolutions project a similar pattern of significant future warming during both winter and summer, and a similar pattern of winter precipitation changes including dominant increase in most areas of northern China and little change or decrease in the southern part. Projected precipitation changes in summer diverge among the three models, especially over eastern China, with a general increase in HadGEM, little change in RegCM at 50 km, and a mix of increase and decrease in RegCM at 25 km resolution. Changes of temperature-related extremes (annual total number of daily maximum temperature > 25 °C, the maximum value of daily maximum temperature, the minimum value of daily minimum temperature in the three simulations especially in the two RegCM simulations are very similar to each other; so are the precipitation-related extremes (maximum consecutive dry days, maximum consecutive 5-day precipitation and extremely wet days’ total amount). Overall, results from this study indicate a very low sensitivity of projected changes in this region to model resolution. While fine resolution is critical for capturing the spatial variability of the control climate, it may not be as important for capturing the climate response to homogeneous forcing (in this case greenhouse gas concentration changes). [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]

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]