Annual Cycle Of Precipitation Research Articles

Extreme Seasonal Droughts and Floods in the Madeira River Basin, Brazil: Diagnosis, Causes, and Trends

The Madeira River, a major tributary of the Amazon River, often undergoes severe flood and drought conditions. This study seeks to investigate the climate processes associated with the opposing extreme precipitation events in the Madeira River basin and to relate them to river discharge variability based on a flood awareness dataset. Despite the uncertainty in the observational datasets, the annual precipitation cycle exhibits a rainy season from November to March. A significant result is the high correlation between the rainy season variability in the Madeira River basin and the sea surface temperature (SST) anomalies in the tropical North Atlantic Ocean and the southwestern South Atlantic Ocean. This result indicates that improving the Atlantic SST representation in climate modeling allows for capturing extreme precipitation events in the region. In addition to this impact, certain Madeira River tributaries present significant climate trends. The river discharge variability reveals an increase in hydrological extremes in recent years in the upper sector, but more significantly, in the lower basin, where it has reduced by more than 400 m3/s per decade. These findings highlight the need to improve in situ data and climate and hydrological modeling, with a focus on describing the intense climate variability and trends in river discharges.

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

AbstractThe present research is aimed at evaluating the climate coupled models with respect to the observational datasets for the investigation of the characteristics of the Indian summer monsoon (ISM). The monthly averaged historical simulations from 12 coupled climate models that participated in Coupled Model Intercomparison Project Phase 6 (CMIP6) are compared with the ground based observational data, satellite and reanalysis datasets for the study period of 1980–2014. This study uses these high‐resolution models to investigate monsoon features, onset and withdrawal dates, primarily focusing on individual model performances rather than highly documented multi‐model mean performance. Performance evaluation of the models suggests that sea surface temperature (SST) simulations by the models are in better agreement for the Arabian Sea than the Bay of Bengal with respect to the ERA5 reanalysis data sets. Further, inter‐model differences amongst the CMIP6 models in estimating the spatial distribution of various monsoon system variables during pre‐monsoon and monsoon seasons are noted which may be attributed to the different model components and varying physics configuration, nonetheless, models like NESM3 and INM‐CN5 are able to reproduce ISM pattern reasonably well. The annual precipitation cycle demonstrates a good agreement between most climate models and IMD data. Evolution of tropospheric temperature gradient (ΔTT) estimated from the CMIP6 models mimics the temporal pattern of the annual rainfall and therefore, is used to estimate the onset and withdrawal dates from CMIP6 models; however, high variability is noted amongst the CMIP6 models in retrieving the onset and withdrawal dates when compared with the IMD observations. Most of the models show a shorter rainy season except NorESM2‐MM. Overall our results suggest that the CMIP6 models can be used for the seasonal mean evaluation of monsoon system parameters and process‐based studies to improve our present understanding of the ISM system.

Precipitation Seasonality Amplifies as Earth Warms

AbstractPrecipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world.

A Key Interaction Behind the Scenes: Dung Beetles in Livestock Production from Arid and Semi-Arid Rangelands.

Pastoralism is a dominant agricultural activity in arid and semi-arid ecosystems. The interaction between coprophagous insects and livestock is a key but an undervalued topic in rangeland management decisions. The objective was to identify similarities and differences in the composition of coprophagous insects and nesting strategies, associated with different climatic conditions within these regions. We performed a literature review of research articles available in the Scopus database which resulted in 17 articles. We examined the taxonomic diversity of dung beetles and their nesting strategies in relation to temperature and precipitation at the study sites. Results revealed a rich and varied interaction between dung beetles and livestock in arid and semi-arid environments, with 364 species from the Scarabaeoidea superfamily documented worldwide. The greatest diversity of dung beetles was reported in hot arid and semi-arid conditions with dry winters and in cold semi-arid ones with no marked annual precipitation cycle. These insects displayed diverse nesting strategies, with endocoprid strategies predominating in colder and drier settings, and paracoprid and telecoprid strategies in warmer, more humid environments. Domestic animal species are currently key components in promoting this interaction, which indicates that the ecological processes involving coprophagous fauna are occurring in production systems, which are under the influence of human decisions. We discuss the design and planning of livestock and rangeland management in arid and semi-arid environments, emphasizing in a more formalized manner the inclusion of this kind of interaction.

How Accurately Are Climatological Characteristics and Surface Water and Energy Balances Represented for the Colombian Amazon Basin?

Context: The Colombian Amazon basin (CAB) is characterized by having many areas with few or no in situ observations. To help fix this shortcoming, there are different gridded datasets that represent meteorological variable estimates from in situ, satellite-based, merged, and model-based products. However, before using them, it is necessary to evaluate their performance.Methods: We analyzed the capacity of five grid datasets to represent the annual cycle, the annual means, and the interquartile range of temperature and the variables that intervene in the surface water and energy balances of the CAB. The five datasets were a regional climate model (REMO) and four reanalyses (ERA40, ERA-Interim, NCEP/NCAR, and NCEP-v2). For the temperature and precipitation analyses, the Delaware, GPCP, CPC, CMAP, and CMAP-v2 datasets were also taken into account. Results: The average annual temperature cycle in the CAB has an unimodal behavior, with higher values between October and March. Only Delaware and ERA-Interim describe this behavior and exhibit values close to those of the observations. The average annual cycle of precipitation is also unimodal, with higher values between March and July. Delaware, GPCP, CPC, and ERA-Interim describe this behavior, although ERA-Interim overestimates precipitation. Additionally, the four reanalyses, especially ERA-Interim, show an unexpected trend of increase in mean annual precipitation. As for the water balance, the precipitation and runoff maps of ERA-Interim and REMO show higher values in the foothills and east of 73 degrees W, a pattern that shows better agreement with the observations than those of the other datasets. ERA-Interim, REMO, and ERA-40 meet the closure condition of both the long-term water and the energy budgets. However, REMO and ERA-Interim overestimated precipitation, ERA-40 underestimated it, and ERA-Interim overestimated solar radiation. This implies that some of the other variables for the water and energy balances are also poorly estimated. Conclusions: The five gridded datasets that allow studying surface water and energy balances are not adequate, so better models and reanalyses are required.

Future changes in mean and extreme precipitation over Peninsular Malaysia using CORDEX-SEA 5 km simulations

The Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia further downscaled three of its 25 km products, i.e. EC-Earth, HadGEM2-ES and MPI-ESM-MR forced regional climate simulations to a higher resolution of 5 km. A newer RegCM4.7 model was used for this exercise. For the simulations over Peninsular Malaysia, analysis shows that these simulations have much smaller precipitation biases. They can correctly predict the annual precipitation cycle over the east coast of Peninsular Malaysia compared to the 25 km simulations. The ensemble of these 5 km simulations indicates that Peninsular Malaysia is expected to experience a decrease in its seasonal mean precipitation, regardless of the RCP 4.5 or RCP 8.5 scenarios. Aside from the reduction of seasonal mean precipitation, consecutive dry and wet days are expected to increase and decrease, respectively. This indicates that Peninsular Malaysia will experience a long dry spell in the future. At the same time, it is suggested that Peninsular Malaysia will have fewer days with very heavy precipitation. Overall, the findings from this study suggest that the 5 km downscaled climate simulations improve significantly over 25 km and that the Peninsular Malaysia region can expect a drier future climate and extremes.

Evaluation of Dynamically Downscaled CMIP6‐CCAM Models Over Australia

AbstractHigh‐resolution climate change projections are increasingly necessary to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using the Conformal Cubic Atmospheric model (CCAM), creating the largest ensemble of high‐resolution downscaled CMIP6 projections for Australia. We compared the host CMIP6 models and downscaled simulations to the Australian Gridded Climate Data (AGCD) observational data and evaluated performance using the Kling‐Gupta efficiency and Perkins skill score. Downscaling improved performance over host GCMs for seasonal temperature and precipitation (10% and 43% respectively), and for annual cycles of temperature and precipitation (6% and 13% respectively). Downscaling also improved the fraction of dry days, reducing the bias for too many low‐rain days. The largest improvements were found in climate extremes, with enhancements to extreme minimum temperatures in all seasons varying from 142% to 201%, and to extreme precipitation of 52% in Austral winter and 47% in summer. The ensemble average integrated skill score improved by 16%. Temperature and precipitation biases were reduced in mountainous and coastal areas. CCAM downscaling outperformed host CMIP6 GCMs at multiple spatial scales and regions—continental Australia, Australian IPCC regions and Queensland's regions—with integrated added value ranging from 9% to 150% and higher over densely populated regions more exposed to climate impacts. This data set will be a valuable resource for understanding future climate changes in Australia.

Least-Squares Triple Cross-Wavelet and Multivariate Regression Analyses of Climate and River Flow in the Athabasca River Basin

Abstract River flow monitoring is a critical task for land management, agriculture, fishery, industry, and other concerns. Herein, a robust least squares triple cross-wavelet analysis is proposed to investigate possible relationships between river flow, temperature, and precipitation in the time–frequency domain. The Athabasca River basin (ARB) in Canada is selected as a case study to investigate such relationships. The historical climate and river flow datasets since 1950 for three homogeneous subregions of the ARB were analyzed using a traditional multivariate regression model and the proposed wavelet analysis. The highest Pearson correlation (0.87) was estimated between all the monthly averaged river flow, temperature, and accumulated precipitation for the subregion between Hinton and Athabasca. The highest and lowest correlations between climate and river flow were found to be during the open warm season and cold season, respectively. Particularly, the highest correlations between temperature, precipitation, and river flow were in May (0.78) for Hinton, July (0.54) for Athabasca, and September (0.44) for Fort McMurray. The new wavelet analysis revealed significant coherency between annual cycles of climate and river flow for the three subregions, with the highest of 33.7% for Fort McMurray and the lowest of 4.7% for Hinton with more coherency since 1991. The phase delay analysis showed that annual and semiannual cycles of precipitation generally led the ones in river flow by a few weeks mainly for the upper and middle ARB since 1991. The climate and river flow anomalies were also demonstrated using the baseline period 1961–90, showing a significant increase in temperature and decrease in precipitation since 1991 for all the three subregions. Unlike the multivariate regression, the proposed wavelet method can analyze any hydrometeorological time series in the time–frequency domain without any need for resampling, interpolation, or gap filling.

Seasonal and Geographical Variations in Fundamental Weather Patterns during Extreme Precipitation as Identified from Omega Equation Forcing

Abstract We investigate the large-scale weather patterns during extreme precipitation (PEx) events over the conterminous United States (CONUS) by applying a version of the quasigeostrophic (QG) omega equation. This work aims to develop a climatology of the weather patterns most related to PEx events during current climate. Extreme events are examined for each of seven regions defined by consistent annual cycles of precipitation and spanning the CONUS. For the CONUS we train several self-organizing maps (SOM) on a pressure–time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each extreme event. The unsupervised learning of the SOM allows us to identify the most descriptive set of nine patterns in vertical velocity associated with precipitation extremes. This method finds multiple frontal- and cyclone-driven patterns while grouping primarily convective events into one pattern. Frontal events include a synoptic pattern consistent with West Coast atmospheric river events as well as pattern groups linked to developing and to mature (“occluded”) frontal cyclones. The primary patterns found during PEx events vary seasonally and geographically. Frontal cyclone patterns are most common during PEx events during summer in the part of the Great Plains and during winter for the Northeast, Southeast, Pacific Northwest, and Southwest. Convection is the most common pattern during summer in all regions. Except in the Southeast, the annual cycles of monthly number of PEx events and average precipitation match well, partially validating our choice of regions to aggregate PEx events.

Assessment of climate simulation over the Tibetan Plateau based on high-resolution multi-RCM within CORDEX-EA-II

The climate simulation over the Tibetan Plateau (TP) remains a challenge for climate models, limiting the reliability of future climate projections over there. This study focuses on the performance of five regional climate models (RCMs) under the Coordinated Regional Climate Downscaling Experiment-East Asia (CORDEX-EA-II) in reproducing the climate over the TP for the period 1981–2015. All simulations are driven by ERA-Interim reanalysis at a resolution of 25 km (0.22°), and are evaluated against the two observation datasets (CN05 and ERA5) as well as inter-compared to identify their weaknesses and differences among them. The results show that all RCMs can capture the observed spatial distribution and annual cycle of temperature and precipitation over the TP, with overall cold and wet biases. The cold biases exhibit a clear altitude dependence, while there is no obvious relationship between precipitation biases and altitude, and most of the RCMs advance the time with the maximum monthly precipitation by 1 month over the northwestern TP. The distribution of temperature trends and the signal of elevation-dependent warming in the observation are well reproduced by most of the RCMs. Inconsistent precipitation trends are found between the two observational datasets during summer and autumn, and the simulated precipitation trends are more consistent with that in ERA5 than in CN05. Further analysis suggests that the downward shortwave radiation is the main contributor to the diverse temperature simulations among the RCMs for all seasons, and the inability of RCMs to reproduce the precipitation trends may be related to the misrepresentation of convection processes. Moreover, some of the RCMs perform better than ERA5 over the TP, demonstrating the importance of dynamical downscaling over the TP.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Precipitation Characteristics across the Three River Headwaters Region of the Tibetan Plateau: A Comparison between Multiple Datasets

Precipitation is crucial for managing water resources in the Three River Headwaters (TRH) region of the Tibetan Plateau (TP). Gridded precipitation datasets across the TRH region exhibit significant discrepancies in their results. Previous studies have primarily focused on assessing average or extreme precipitation for a single dataset or several datasets. In this study, based on the observed gridded precipitation dataset (CN05.1), a comprehensive evaluation of the climatic features and extreme precipitation across the TRH region from 1983 to 2014 is performed by employing two gauge-based gridded datasets (GPCC and CRU), two satellite-derived precipitation datasets (P-CDR and IMERG), and two reanalysis precipitation datasets (ERA5 and CRA40). The results show that all datasets are consistent in reproducing the climatology, interannual variability, and annual cycle of precipitation in the TRH region. However, the different datasets exhibit significant discrepancies in characterizing the long-term trends and extreme precipitation events. P-CDR and GPCC provide a good representation of the spatial variability of the annual mean climatology. ERA5 and CRU are more reliable in capturing interannual variabilities. The long-term trends can be closely described by employing CRU. P-CDR and GPCC exhibit higher skills in terms of the annual cycle. P-CDR performs better than IMERG for daily precipitation in terms of probability distributions and other assessment metrics. P-CDR and IMERG have advantages and disadvantages in characterizing the nine extreme precipitation indices. This study demonstrates a comprehensive comparison method using multiple precipitation datasets to gain essential insight into the strengths and weaknesses of various datasets across the TRH region.

Coherency and phase delay analyses between land cover and climate across Italy via the least-squares wavelet software

Land cover and climate monitoring is a crucial task in agriculture, forestry, hazard management, and ecosystems assessment. In this paper, normalized difference vegetation index (NDVI), land surface temperature (LST), and land cover products by the moderate resolution imaging spectroradiometer (MODIS) as well as precipitation were utilized to monitor the spatiotemporal dynamics of vegetation and climate along with their correlation and coherency across Italy during 2000–2021. The analyses were performed on both pixel and ecoregion levels via the least-squares wavelet software (LSWAVE). It was found that relatively more areas in all ecoregions had positive NDVI gradients than negative for each month since 2000. It was estimated that the average NDVI has increased by ∼0.07 since 2000 for all ecoregions. Except the southern ecoregion which showed an insignificant daytime cooling, other ecoregions have been warming by less than 0.05 °C/year since 2000. Furthermore, precipitation had an insignificant decreasing trend for almost all ecoregions over the past two decades. The annual coherency between NDVI and LST was found much stronger than the annual coherency between NDVI and precipitation. The annual cycles of NDVI and LST were out-of-phase for the southern ecoregion while the annual cycle of precipitation led the one in NDVI by about one month for this ecoregion, the only ecoregion showing the highest Pearson correlation (53%) and annual coherency (39%) between NDVI and precipitation. For other ecoregions, the annual cycles of NDVI and LST were approximately in-phase, i.e., less than a month phase delay.

Evaluation of the convection-permitting regional climate model CNRM-AROME41t1 over Northwestern Europe

Since a decade, convection-permitting regional climate models (CPRCM) have emerged showing promising results, especially in improving the simulation of precipitation extremes. In this article, the CPRCM CNRM-AROME developed at the Centre National de Recherches Météorologiques (CNRM) since a few years is described and evaluated using a 2.5-km 19-year long hindcast simulation over a large northwestern European domain using different observations through an added-value analysis in which a comparison with its driving 12-km RCM CNRM-ALADIN is performed. The evaluation is challenging due to the lack of high-quality observations at both high temporal and spatial resolutions. Thus, a high spatio-temporal observed gridded precipitation dataset was built from the collection of seven national datasets that helped the identification of added value in CNRM-AROME. The evaluation is based on a series of standard climatic features that include long-term means and mean annual cycles of precipitation and near-surface temperature where CNRM-AROME shows little improvements compared to CNRM-ALADIN. Additional indicators such as the summer diurnal cycle and indices of extreme precipitation show, on the contrary, a more realistic behaviour of the CNRM-AROME model. Moreover, the analysis of snow cover shows a clear added-value in the CNRM-AROME simulation, principally due to the improved description of the orography with the CPRCM high resolution. Additional analyses include the evaluation of incoming shortwave radiation, and cloud cover using satellite estimates. Overall, despite some systematic biases, the evaluation indicates that CNRM-AROME is a suitable CPRCM that is superior in many aspects to the RCM CNRM-ALADIN.

Evaluación del índice de precipitación estandarizado (SPI) con datos de alta resolución en clima histórico y futuro en el centro de México

Introduction: Drought monitoring needs good quality meteorological data, but records frequently face problems. Therefore, satellite precipitation datasets are an alternative. Objective: Evaluation of three high-resolution datasets (compared with observed precipitation) to capture wet and dry periods. In addition, the influence of the selection of the dataset to downscale future climate simulations is evaluated in the estimation of drought indicators. Methodology: Precipitation products (NOAA, CHIRPS and PERSIANN-CDR) were compared with observations from seven meteorological stations (1983-2013). The 12-month standard precipitation index (SPI) was selected to evaluate drought conditions. Also, precipitation estimates from CanESM2 were downscaled and bias-corrected with each dataset. Results: The annual precipitation cycle is well capture, but underestimation is noted. The datasets have a good correlation, but less variability. Regarding the SPI, results show good correlation, but extremely dry events are generally underestimated with CHIRPS and PERSIANN-CDR. The NOAA dataset performs better in terms of categorical scores, especially for wet events. Similar median values were found in the drought indicators in future; however, the datasets lead to less variability than observations, especially in the drought frequency indicator. Study limitations: Limited number of good quality meteorological records. Originality: Selected precipitation datasets are tested under different climatic conditions for the first time in Mexico. Also, the influence of the reference precipitation dataset for bias correction is evaluated on future drought projections. Conclusions: Precipitation products should be tested before their use in monitoring droughts in the historical period, as well as in estimating droughts with future projections.

Performance of the Weather Research and Forecasting model in simulating climate over northern Asia

AbstractHigh‐resolution simulation is important for understanding climate change and its impact over regions with complex terrain. Northern Asia (NA) is such a region and sensitive to global warming. However, the performance of the regional model over NA is still unclear. In this study, we conducted a two‐decade‐long high‐resolution (25 km) simulation over NA using the Weather Research and Forecasting (WRF) model forced by the ERA‐Interim reanalysis and then evaluated the performance of WRF against the observations. The results show that the WRF simulation, set up with selections of the physical schemes, can reasonably reproduce the spatial and temporal features of surface air temperature and precipitation over NA. In addition, the WRF simulation can add more regional details on the climate features than the ERA‐Interim data, especially over the NA regions with complex terrain. The spatial‐pattern correlation coefficient of summer precipitation over NA between the WRF simulation and observations is higher than that of the ERA‐Interim data. WRF can reasonably reproduce the annual cycles of precipitation. Moreover, the observed year‐to‐year variations in summer surface air temperature and precipitation over most NA subregions could be generally captured by the WRF. In conclusion, although systemic model bias still exists, the evaluations in this study indicate that WRF shows reasonable performance in a high‐resolution simulation of climate over NA, consequently providing a scientific basis for future simulation studies over the region.

Revisiting large-scale interception patterns constrained by a synthesis of global experimental data

Abstract. Rainfall interception loss remains one of the most uncertain fluxes in the global water balance, hindering water management in forested regions and precluding an accurate formulation in climate models. Here, a synthesis of interception loss data from past field experiments conducted worldwide is performed, resulting in a meta-analysis comprising 166 forest sites and 17 agricultural plots. This meta-analysis is used to constrain a global process-based model driven by satellite-observed vegetation dynamics, potential evaporation and precipitation. The model considers sub-grid heterogeneity and vegetation dynamics and formulates rainfall interception for tall and short vegetation separately. A global, 40-year (1980–2019), 0.1∘ spatial resolution, daily temporal resolution dataset is created, analysed and validated against in situ data. The validation shows a good consistency between the modelled interception and field observations over tall vegetation, both in terms of correlations and bias. While an underestimation is found in short vegetation, the degree to which it responds to in situ representativeness errors and difficulties inherent to the measurement of interception in short vegetated ecosystems is unclear. Global estimates are compared to existing datasets, showing overall comparable patterns. According to our findings, global interception averages to 73.81 mm yr−1 or 10.96 × 103 km3 yr−1, accounting for 10.53 % of continental rainfall and approximately 14.06 % of terrestrial evaporation. The seasonal variability of interception follows the annual cycle of canopy cover, precipitation, and atmospheric demand for water. Tropical rainforests show low intra-annual vegetation variability, and seasonal patterns are dictated by rainfall. Interception shows a strong variance among vegetation types and biomes, supported by both the modelling and the meta-analysis of field data. The global synthesis of field observations and the new global interception dataset will serve as a benchmark for future investigations and facilitate large-scale hydrological and climate research.

Wavelet-based spatiotemporal analyses of climate and vegetation for the Athabasca river basin in Canada

Monitoring spatiotemporal changes in climate and vegetation coverage are crucial for various purposes, including water, hazard, and agricultural management. Climate has an impact on vegetation, however, studying their relationship is challenging. We implemented the Least-Squares Wavelet (LSWAVE) software for investigating trend, coherency, and time lag estimation between climate and vegetation time series. We utilized Normalized Difference Vegetation Index (NDVI) time series provided by the Terra satellite and hybrid climate time series. We found that the seasonal cycles of climate and NDVI are coherent with time delay. For the entire Athabasca River Basin (ARB), the most coherent component was the annual cycle with 84% annual coherency between vegetation and temperature and 46% between vegetation and precipitation. The annual cycles of temperature and precipitation led the ones in vegetation by about two and three weeks, respectively. Relatively lower coherency was observed in the mountainous region (upper ARB) and higher coherency in the middle ARB. From the cross-spectrograms, a clear time delay pattern was observed between the annual cycles of climate and vegetation since 2000 but not for other high-frequency seasonal cycles. The results also highlighted the advantages of LSWAVE algorithms over traditional algorithms, such as linear regression and correlation. Furthermore, we analyzed the annual land use and land cover data provided by the Terra and Aqua satellites and discussed their linkage with the climate and NDVI results.

Signatures of midsummer droughts over Central America and Mexico

The annual cycle of precipitation over most parts of Central America and southern Mexico is climatologically characterized by a robust bimodal distribution, normally termed as the midsummer drought (MSD), influencing a large range of agricultural economic and public insurances. Compared to studies focusing on mechanisms underpinning the MSD, less research has been undertaken related to its climatological signatures. This is due to a lack of generally accepted methods through which to detect and quantify the bimodal precipitation accurately. The present study focuses on characterizing the MSD climatological signatures over Central America and Mexico using daily precipitation observations between 1979 and 2017, aiming to provide a comprehensive analysis of MSD in fine scale over this region. This was completed using a new method of detection. The signatures were analyzed from three aspects, namely (1) climatological mean states and variability; (2) connections with large scale modes of climate variability (El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO)); and (3) the potential afforded by statistical modelling. The development of MSDs across the region is attributed to changes of surface wind–pressure composites, characterized by anomalously negative (positive) surface pressure and onshore (offshore) winds during the peak (trough) of precipitation. ENSO’s modulation of MSDs is also shown by modifying the surface wind–pressure patterns through MSD periods, inducing the intensified North Atlantic Subtropical High and associated easterlies from the Caribbean region, which induce relatively weak precipitation at corresponding time points and subsequently intensify the MSD magnitude and extend the MSD period. Building on previous research which showed MSDs tend to start/end in MJO phases 1 and 8, a fourth–order polynomial was used here to statistically model the precipitation time series during the rainy season. We show that the strength of the bimodal precipitation can be well modelled by the coefficient of the polynomial terms, and the intra-seasonal variability is largely covered by the MJO indices. Using two complete MJO cycles and the polynomial, the bimodal precipitation during the rainy season over Central America and Mexico is synoptically explained, largely contributing to our understanding of the MJO’s modulation on the MSD.

Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6

The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.