Monthly Time Scales Research Articles

Extreme precipitation stable isotopic compositions reveal unexpected summer monsoon incursions in the Qilian Mountains

Isotope composition and moisture sources of precipitation are important for understanding water cycles and reconstructing paleoclimate. Based on 15-years' precipitation stable Isotope composition (δ18O and δ2H) from four stations of the Qilian Mountains, we found unique δ18O and δ2H features associated with the incursion of the summer monsoon over the Qilian Mountains, northwestern China. In 12 of the 15 years, similar seasonal variations of δ18O and δ2H confirmed a dominant source of moisture from Westerly circulation, and higher intercepts of the local meteoric water line (LMWL) indicated strong recycling of continental moisture. However, in August 2016 and 2018, extremely low slopes and intercepts of the LMWL, and more negative δ18O and δ2H revealed substantial contributions of the Asian summer monsoon to precipitation of the Qilian Mountains, with extremely heavy precipitation in August 2016. The column moisture flux, land-sea thermal contrast, correlations of precipitation δ18O with East Asian Summer Monsoon Index and Westerlies Index, HYSPLIT modeling results and precipitation δ18O along backward trajectories confirmed incursions of the summer monsoon in August 2016 and 2018. Our redefining of the boundary of the summer monsoon region confirmed the summer monsoon incursion zone can extend to the west of longitude 96°E and north of latitude 40°N in strong monsoon years, corresponding to boundaries of monsoon incursions in the mid-Holocene. Temperature correlated with precipitation δ18O at monthly and shorter time scales, but not for whole seasons or at yearly scale, revealing that summer monsoon incursions are therefore more likely than changing temperature to explain the multi-year cycles in the Qilian Mountains ice archives. Continent-scale shifts in atmospheric circulation strongly influence water resources in the Qilian mountains, and may change in frequency as climate warms. This study therefore has important implications for understanding water resources in the Qilian mountains in the past and into the future.

Validation of high-resolution satellite precipitation products over West Africa for rainfall monitoring and early warning

Satellite rainfall estimation products (SRPs) can help overcome the absence of rain gauge data to monitor rainfall-related risks and provide early warning. However, SRPs can be subject to several sources of errors and need to be validated before specific uses. In this study, a comprehensive validation of nine high spatial resolution SRPs (less than 10 km) was performed on monthly and dekadal time scales for the period 2001–2015 in West Africa. Both SRPs and reference data were remapped to a spatial resolution of 0.1 ° and the validation process was carried out on a grid scale, with 1,202 grids having at least one rain gauge throughout West Africa. Unconditional statistical metrics, such as mean absolute error, Pearson correlation, Kling-Gupta efficiency and relative bias, as well as the reproducibility of rainfall seasonality, were used to describe the agreement between SRPs and reference data. The PROMETHEE II multi-criteria decision analysis (MCDA) method was employed to rank SRPs by considering criteria encompassing both their intrinsic characteristics and performance metrics. Overall, IMERGv6-Final, MSWEPv2.2, RFE2, ARC2, and TAMSATv3.1, performed reasonably well, regardless of West African climate zones and rainy season period. Given the performances displayed by each of these SRPs, RFE2, ARC2, and MSWEPv2.2 would be suitable for drought monitoring. TAMSATv3.1, IMERGv6-Final, RFE2, ARC2, and MSWEPv2.2 are recommended for comprehensive basin water resources assessments. TAMSATv3.1 and MSWEPv2.2 would be of interest for the characterization of variability and long-term changes in precipitation. Finally, TAMSATv3.1, ARC2, and MSWEPv2.2, could be good alternatives to observed data as predictants in West African Regional Climate Outlook Forum (RCOF) process.

Meteorological drought mitigation for combating climate change: a case study of southern Sindh, Pakistan

A meteorological drought study is performed using monthly time scale data from three separate locations in southern Sindh, Pakistan. Rainfall and temperature have been used to identify the drought. These data were transformed into drought indices known as the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI), which were derived using (the Hargreaves equation). In this study, two indices are compared for three separate meteorological stations Chhor, Mithi, and Badin where most socioeconomic livelihoods depend heavily on water. The SPEI is produced through a simple water balance combining precipitation and temperature, in distinction to the SPI, it just considers precipitation. In conclusion, our study showed that both indices were capable of detecting droughts that fluctuated in time across the reference period of 2004–2021. SPI and SPEI's direction of change was similar, however the impact on the drought condition varied. SPEI discovered more droughts with longer durations and greater with 13 moderate droughts at SPEI-3 for Chhor and Badin Station while Mithi indicated 8 moderate droughts during 2004-2021 and SPI-3 indicated 4 moderates for Chhor, Mithi and Badin indicated 6 moderate drought. Conversely, SPEI discovered more moderate-level droughts than SPI, however they were of shorter length and less frequent occurrence than the severe to moderate droughts. The findings imply that drought characteristics are significantly influenced by temperature variability.

Analysis of variations and trends of temperature over Niger central hydrological area, Nigeria, 1911–2015

Adequate information about the variations and trends of climatic variables is indispensable for the better management of the water resources and agricultural sectors. In this study, the variability and trends of the monthly, seasonal and annual temperatures ((TMIN, TMAX and TMEAN) time series were studied over Niger Central Hydrological Area, Nigeria (NCHA) during 1911–2015 using the Climate Research Unit (CRU) data. Different statistical operations analysis comprising descriptive statistics, linear regression model, Mann–Kendall test, Pettitt's test and standard normal homogeneity test (SNHT) were employed for the analysis. The tests were used to examine the temperature variability, presence of monotonic trends and change points of the time series. The monthly TMEAN variability over the entire area was between 2.10% and 3.61%. Periods 1914–1925 and 1974–1980 showed cooling trends, while 1930–1935, 1981–1988 and 2001–2015 exhibited warming trends. The annual TMIN and TMEAN showed positive trends across the 33 sub-basins of NCHA, while the TMAX showed mixed trends. The increasing trend was most significant on the monthly, seasonal and annual timescales of the TMIN, followed by TMEAN. In contrast, the mixed trends in TMAX were not significant. The global rate of change trends of TMIN, TMAX and TMEAN are 0.091, −0.007 and 0.043 °C/decade respectively. The overall warming of TMEAN also had a significant upward shift change of 0.43 °C since 1980 and the change spatially is characterized by a SW-NE orientation. There is a need to embrace green settlement and a green economy to reduce the impact of the ever-increasing warming trend over the region.

MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called MetaFlux. The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

Novel evaluation of sub‐seasonal precipitation ensemble forecasts for identifying high‐impact weather events associated with the Indian monsoon

AbstractWe assess the skill of the fully coupled lagged ensemble forecasts from GloSea5‐GC2, for the sub‐seasonal to seasonal (S2S) timescale up to 4 weeks, with the aim of understanding how these forecasts might be used in a Ready‐Set‐Go style decision‐making framework. Integrated Multi‐satellite Retrievals for Global Precipitation Measurement (IMERG‐GPM) are used to seamlessly verify these ensemble forecasts up to monthly timescales whereby forecast and observed precipitation fields are summed over a sequence of increasing lead time accumulation windows (LTAWs), from 1d1d up to 2w2w. Results show that model biases grow with increasing LTAW and with ensemble member age. The S2S model exhibits both wet and dry biases across different parts of the Indian domain. The S2S model error grows from around 10 mm for a 24‐h accumulation to 50 mm for the 2‐week LTAWs. The actual skill and potential skill of the ensemble forecasts reveal that the potential skill is not always greater than actual skill everywhere. The sensitivity to the number and age of ensemble members was tested, with potential skill showing more impact from the exclusion of older members at all LTAWs. We conclude that the older lagged members do not necessarily add value by being included in the short to medium range or even for the extended range forecasts. GloSea5‐GC2 shows some skill in detecting large accumulations, which are not always tied to locations where they are climatologically frequent.

Updated observations of clouds by MODIS for global model assessment

Abstract. This paper describes a new global dataset of cloud properties observed by MODIS relying on the current (collection 6.1) processing of MODIS data and produced to facilitate comparison with results from the MODIS observational proxy used in climate models. The dataset merges observations from the two MODIS instruments into a single netCDF file. Statistics (mean, standard deviation, and number of observations) are accumulated over daily and monthly timescales on an equal-angle grid for viewing and illumination geometry, cloud detection, cloud-top pressure, and cloud properties (optical thickness, effective particle size, and water path) partitioned by thermodynamic phase and an assessment as to whether the underlying observations come from fully or partly cloudy pixels. Similarly partitioned joint histograms are available for (1) optical thickness and cloud-top pressure, (2) optical thickness and particle size, and (3) cloud water path and particle size. Differences with standard data products, caveats for data use, and guidelines for comparison to the MODIS simulator are described. Data are available on daily (https://doi.org/10.5067/MODIS/MCD06COSP_D3_MODIS.062; NASA, 2022b) and monthly (https://doi.org/10.5067/MODIS/MCD06COSP_M3_MODIS.062; NASA, 2022a) timescales.

Variability of extragalactic X-ray jets on kiloparsec scales

Unexpectedly strong X-ray emission from extragalactic radio jets on kiloparsec scales has been one of the major discoveries of Chandra, the only X-ray observatory capable of sub-arcsecond-scale imaging. The origin of this X-ray emission, which appears as a second spectral component from that of the radio emission, has been debated for over two decades. The most commonly assumed mechanism is inverse Compton upscattering of the Cosmic Microwave Background (IC-CMB) by very low-energy electrons in a still highly relativistic jet. Under this mechanism, no variability in the X-ray emission is expected. Here we report the detection of X-ray variability in the large-scale jet population, using a novel statistical analysis of 53 jets with multiple Chandra observations. Taken as a population, we find that the distribution of p-values from a Poisson model is strongly inconsistent with steady emission, with a global p-value of 1.96e-4 under a Kolmogorov-Smirnov test against the expected Uniform (0,1) distribution. These results strongly imply that the dominant mechanism of X-ray production in kpc-scale jets is synchrotron emission by a second population of electrons reaching multi-TeV energies. X-ray variability on the time-scale of months to a few years implies extremely small emitting volumes much smaller than the cross-section of the jet.

Satellite precipitation bias estimation and correction using in situ observations and climatology isohyets for the MENA region

The availability of reliable gridded precipitation datasets is limited around the world, especially in arid regions. In this study, we utilized observations from satellite-based precipitation data and in situ rain gauge observations to determine a suitable precipitation dataset in the Middle East & North Africa (MENA) region. First, we evaluated seven different precipitation products using rain gauge observations. The validation was conducted at the daily, monthly, and annual time scales. Results indicated a weaker correlation between in situ rain gauge observation and satellite precipitation data at the daily time step (r: 0.02 to 0.44), mainly due to the lack of range in precipitation distribution. However, the agreement between precipitation estimates and in situ gauge observations improved at monthly (r: 0.02 to 0.66) and annual time scales (r: −0.22 to 0.57), indicating greater reliability of satellite-based precipitation at monthly and annual time scales. Based on the results and dataset availability, the Multi-Source Weighted-Ensemble Precipitation (MSWEP) was deemed suitable to create a bias-corrected new precipitation dataset for the MENA region. This study highlights the benefits of an adjusted regional precipitation product for hydrologic applications in the MENA region, such as streamflow or runoff estimation, to improve the reliability of the model outputs.

Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach

Nonlinear contributions from climate change and anthropogenic activity to the Normalized Difference Vegetation Index (NDVI) are analyzed to better understand the mechanisms underlying the nonlinear response of vegetation growth. In this study, it was hypothesized that NDVI dynamics on a nonlinear trajectory could track fluctuations of climate change and anthropogenic activity. Contributions from climate change and anthropogenic activity to NDVI were quantified using a locally weighted regression approach based on monthly timescale datasets. The findings showed that: 1) Vegetation cover fluctuated and increased in 81% of regions in China from 2000 to 2019. 2) The average predicted nonlinear contribution (APNC) of anthropogenic activity to NDVI was positive in China. The temperature APNC was positive in most of China but negative in Yunnan, where high temperatures and asynchronous temporal changes in temperature and NDVI were observed. The precipitation APNC was positive in the north of the Yangtze River, where precipitation is insufficient; but negative in South China, where precipitation is plentiful. Anthropogenic activity had the highest magnitude among the three nonlinear contributions, followed by temperature and precipitation. 3) The regions with contribution rates of anthropogenic activity greater than 80% were mainly distributed in the central Loess Plateau, North China Plain, and South China, while the areas with contribution rates of climate change greater than 80% were mainly concentrated in the northeastern QTP, Yunnan, and Northeast China. 4) The high temperature, drought, and asynchronous temporal changes in temperature, precipitation, and NDVI caused the negative average of changing trends in the predicted nonlinear contribution (PNC) of climate change to NDVI. Deforestation, land cover change, and grazing/fencing led to the negative average of changing trends in PNC from anthropogenic activity. These findings deepen our understanding of the mechanisms underlying the nonlinear responses of vegetation growth to climate change and anthropogenic activity.

Evaluations of satellite/reanalysis rainfall and temperature products in the Bale Eco-Region (Southern Ethiopia) to enhance the quality of input data for hydro-climate studies

The sparse distribution and lack of meteorological stations due to deficit infrastructure in developing countries is one of the limiting factors for hydro-climate studies, and dependency on globally available data is often prone to various level of errors. Thus, this study aimed to evaluate the performance of satellite/reanalysis rainfall and temperature products in the Bale Eco-Region (BER) in Southern Ethiopia. This study evaluated performances of three rainfall products such as the Climate Hazards Group Infrared Precipitation with Stations, version 2.0 (CHIRPS v2.0), Tropical Applications of Meteorology using SATellite and ground-based observations, version 3.1 (TAMSAT v3.1) and Multi-Source Weighted-Ensemble Precipitation, version 2.8 (MSWEP v2.8). The two temperature products evaluated in this study are ERA5 and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA v2). Evaluations of these satellite/reanalysis rainfall and temperature products were undertaken against observed data (1995–2014) in temperate, sub-tropical and tropical agro-ecological zones (AEZs) across multiple temporal scales ranging from the daily to annual. For assessing the performances of satellite/reanalysis rainfall and temperature products, a point-pixel evaluation approach was undertaken using five continuous evaluation scores such as correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), percent bias (PBIAS) and Kling–Gupta efficiency (KGE). Categorical sores such as Probability of Detection (POD), False Alarm Ratio (FAR) and Frequency Bias Index (FBI) were also used for assessing the rainfall products. The findings revealed that MSWEP v2.8 has better performance than CHIRPS v2.0 and TAMSAT v3.1 in temperate and tropical AEZs at the daily, dry season and annual time steps as well as in sub-tropical AEZ in dry season and annual temporal periods, but MSWEP v2.8 displayed comparable performance with TAMSAT v3.1 in the daily time step. CHIRPS v2.0 exhibit superior performance in the monthly time scale in the three AEZs as well as in the wet season in temperate and tropical AEZs, but TAMSAT v3.1 has outperformed than CHIRPS v2.0 in the wet season in sub-tropical AEZ. The finding also indicated that the capability of MSWEP v2.8 to detect the rainy days (79–86%) and frequency of rainy days (0.99–1.79) is better than CHIRPS v2.0 and TAMSAT v3.1, but TAMSAT v3.1 has shown the best performance for identifying the non-rainy days (14–38%) than MSWEP v2.8 and CHIRPS v2.0. With regard to temperature, MERRA v2 outperformed over ERA5 in temperate and tropical AEZs for estimating both maximum and minimum temperatures from the daily to annual time scales, but ERA5 has shown superior performance than MERRA v2 in the sub-tropical AEZ. In view of the finding, we concluded that the best performing rainfall and temperature products for each AEZ can be used for data scarce regions such as the BER. The findings of this study provide important insights about the need to identify best performing rainfall and temperature products for different AEZs to enhance the quality of hydro-climate study outputs in the decision-making process.

Re-evaluation of the effects of precipitation amount and temperature on precipitation δ18O at the monthly and interannual timescales

Abstract Proxy records of the oxygen isotope ratio of 18O/16O of past precipitation (δ18Op) have played an important role in revealing past hydroclimatic changes, on the basis of global observed relationships between monthly precipitation δ18Op and both precipitation amount and temperature only of a few years as reported by Dansgaard in 1964. It is therefore crucial to systematically re-evaluate the relationships using modern instrumental data. We analysed monthly and annual mean correlations from 108 global stations over the past about 60 years. Consistent with previously reported results, monthly δ18Op values in the high latitudes (≥60°) show a significant positive correlation with temperature (referred to as ‘temperature effect’) and a negative trend with precipitation amount in the low latitudes (≤20°) (‘amount effect’). However, these correlations do not hold true for yearly mean data for more than three-quarters of the stations evaluated. This indicates that the relationships between the different temporal resolutions could be more complicated than previously thought. For the related natural archives, such as ice cores, sediments, and carbonates, further careful evaluation is required to establish the robustness of their paleoclimatic implications.

Moisture Sources and Rainfall δ18O Variability over the Central Andes of Peru—A Case Study from the Mantaro River Basin

The Mantaro River Basin is one of the most important regions in the central Peruvian Andes in terms of hydropower generation and agricultural production. Contributions to better understanding of the climate and hydrological dynamics are vital for this region and constitute key information to support regional water security and socioeconomic resilience. This study presents eight years of monthly isotopic precipitation information (δ18O, Dxs) collected in the Mantaro River Basin. The isotopic signals were evaluated in terms of moisture sources, including local and regional climatic parameters, to interpret their variability at monthly and interannual timescales. It is proposed that the degree of rainout upstream and the transport history of air masses, also related to regional atmospheric features, are the main factors influencing the δ18O variability. Moreover, significant correlations with precipitation amount and relative humidity imply that local processes in this region of the Andes also exert important control over isotopic variability. Two extreme regional climate events (the 2010 drought and the 2017 coastal El Niño) were evaluated to determine how regional atmospheric circulation affects the rainfall isotope variability. Based on these results, recommendations for hydroclimate studies and paleoclimate reconstructions are proposed in the context of the Mantaro River Basin. This study intends to encourage new applications considering geochemical evidence for hydrological studies over the central Andean region.

The rate of information transfer as a measure of ocean–atmosphere interactions

Abstract. Exchanges of mass, momentum and energy between the ocean and atmosphere are of large importance in regulating the climate system. Here, we apply for the first time a relatively novel approach, the rate of information transfer, to quantify interactions between the ocean surface and the lower atmosphere over the period 1988–2017 at a monthly timescale. More specifically, we investigate dynamical dependencies between sea surface temperature (SST), SST tendency and turbulent heat flux in satellite observations. We find a strong two-way influence between SST and/or SST tendency and turbulent heat flux in many regions of the world, with the largest values in the eastern tropical Pacific and Atlantic oceans, as well as in western boundary currents. The total number of regions with a significant influence by turbulent heat flux on SST and on SST tendency is reduced when considering the three variables (this case should be privileged, as it provides additional sources of information), while it remains large for the information transfer from SST and SST tendency to turbulent heat flux, suggesting an overall stronger ocean influence compared to the atmosphere. We also find a relatively strong influence by turbulent heat flux taken 1 month before on SST. Additionally, an increase in the magnitude of the rate of information transfer and in the number of regions with significant influence is observed when looking at interannual and decadal timescales compared to monthly timescales.

The magnetic field and multiple planets of the young dwarf AU Mic

ABSTRACT In this paper, we present an analysis of near-infrared spectropolarimetric and velocimetric data of the young M dwarf AU Mic, collected with SPIRou at the Canada–France–Hawaii telescope from 2019 to 2022, mostly within the SPIRou Legacy Survey. With these data, we study the large- and small-scale magnetic field of AU Mic, detected through the unpolarized and circularly polarized Zeeman signatures of spectral lines. We find that both are modulated with the stellar rotation period (4.86 d), and evolve on a time-scale of months under differential rotation and intrinsic variability. The small-scale field, estimated from the broadening of spectral lines, reaches 2.61 ± 0.05 kG. The large-scale field, inferred with Zeeman–Doppler imaging from Least-Squares Deconvolved profiles of circularly polarized and unpolarized spectral lines, is mostly poloidal and axisymmetric, with an average intensity of 550 ± 30 G. We also find that surface differential rotation, as derived from the large-scale field, is ≃30 per cent weaker than that of the Sun. We detect the radial velocity (RV) signatures of transiting planets b and c, although dwarfed by activity, and put an upper limit on that of candidate planet d, putatively causing the transit-timing variations of b and c. We also report the detection of the RV signature of a new candidate planet (e) orbiting further out with a period of 33.39 ± 0.10 d, i.e. near the 4:1 resonance with b. The RV signature of e is detected at 6.5σ while those of b and c show up at ≃4σ, yielding masses of $10.2^{+3.9}_{-2.7}$ and $14.2^{+4.8}_{-3.5}$ M⊕ for b and c, and a minimum mass of $35.2^{+6.7}_{-5.4}$ M⊕ for e.

Case study of rainfall and temperature assessment through trend and homogeneity analyses in Vadodara and Chhotaudepur district of Gujarat State, India

This study aims to assess the climate change impact on the rainfall and temperature data of the Vadodara-Chhotaudepur district of India and to focus on the environmental challenges related to the rainfall and temperature in the present state of development, land use, industrialization, and urbanization. The study utilized nine trend analysis methods, namely linear regression (LR), Sen's robust slope estimator (SS), Mann-Kendall test (MK), Spearman's rank correlation (SRC), the trend-free pre-whitening (TFPW), variance correction approach by Hamed and Rao (1998) with MK test (MK-CF1), variance correction approach by Yue and Wang (2004) with MK test (MK-CF2), block bootstrap with MK test (BBS-MK), and graphical method as innovative trend analysis (ITA), applied on monthly, annual, and seasonal scales. Additionally, the study also employed four homogeneity analysis methods, including Pettitt's test, standard normal homogeneity test (SNHT), Buishand's test, and Von Neumann Ratio test (VNRTs). The IMD (Indian meteorological department) gridded long-term rainfall data from 1901 to 2019 and temperature data from 1951 to 2019 are used in the present study to assess the homogeneity and trends of the data series. Results showed a warming trend of maximum temperature (MaxT) and minimum temperature (MinT) at the monthly, annual, and seasonal time scale and significant (at 5%) warming trend in annual MinT in the entire study area. Annual rainfall showed negative trend in the study area with significant (at 5%) negative trend in eastern deforested area western industrial and area adjacent to thermal power station. The change point is detected in annual rainfall time series in eastern forest area in 1959 and western area in 1983, i.e., after development of the industries and commissioning of thermal power station in the western study area. The trend rate of MaxT and MinT has been recognized as 0.004°C/year and 0.019°C/year, respectively, for the data period of 1951 to 2019. The annual rainfall trend rate has been observed as -0.743mm/year for the data range 1901 to 2019. All trend analysis methods revealed consistent results except MK-CF2 method, which portraits greater number of significant trends in trend analysis methods.

A Comparison of the Statistical Downscaling and Long-Short-Term-Memory Artificial Neural Network Models for Long-Term Temperature and Precipitations Forecasting

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models’ output, to improve water management and long-term temperature and precipitations forecasting at local level.

Monthly Arctic Sea‐Ice Prediction With a Linear Inverse Model

AbstractWe evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. We find that more than 500 years of training data and 100 years of validation data are needed to reliably estimate LIM forecast skill. The best LIM has skill up to 8 months lead time and outperforms an autoregressive model of order one (AR1) forecast at all locations, with particularly large outperformance near the ice edge. However, for out‐of‐sample validation tests using data from various different model simulations and reanalysis products, they underperform an AR1 model due to differences in the location of the sea‐ice edge from the training data. We present a metric for predicting LIM forecast skill, based on the spatial correlation of the variance in the training and validation data sets.

The largest bright ULX population in a galaxy: X-ray variability and luminosity function in the Cartwheel ring galaxy

ABSTRACT We analyse all the available Chandra observations of the Cartwheel galaxy and its compact group, taken between 2001 and 2008, with the main aim of addressing the variability in the X-ray band for this spectacular collisional ring galaxy. We focus on the study of point-like sources, in particular we are interested in ultraluminous X-ray sources (ULXs; LX ≥ 1039 erg s−1), that we treat as a class. We exploit archival XMM–Newton data to enrich the study of the long-term variability, on time-scales of months to years. We find a total of 44 sources in the group area, of which 37 in total are ULXs positionally linked with the galaxies and of which we can study variability. They are 29 in the Cartwheel itself, seven in G1, and one in G3. About one-third of these 37 sources show long-term variability, while no variability is detected within the single observations. Of those, five ULXs have a transient behaviour with a maximum range of variability (Lmax/Lmin) of about one order of magnitude and are the best candidate neutron stars. The X-ray luminosity function (XLF) of the point-like sources remains consistent in shape between the Chandra observations both for the Cartwheel galaxy itself and for G1, suggesting that flux variability does not strongly influence the average properties of the population on the observation time-scales.

A framework for estuarine future sea-level scenarios: Response of the industrialised Elbe estuary to projected mean sea level rise and internal variability

In this study, we apply probabilistic estimates of mean sea level (MSL) rise and a sub-set of regional climate model ensemble simulations to force a numerical model of the southern North Sea, downscaling projected sea level variability to the Elbe estuary that serves as a prototype for an industrialised meso-tidal estuary. The specific forcing combination enables a localised projection of future estuarine hydrodynamics accounting for the spread of projected global sea level rise and the spread of the regional climate projection due to internal variability. Under the applied high-emission scenario, the Elbe estuary shows high decadal rates of mean water level (MWL) rise beyond 19 mm y-1, increase in the tidal range of up to 14 mm y-1 and increase in extreme water levels of up to 18 mm y-1. The bandwidth of the estuarine response is also high. For example, the range of average monthly extreme water levels is up to 0.57 m due to the spread of projected global sea level rise, up to 0.58 m due to internal variability whereas seasonal range attains 1.99 m locally. In the lower estuary, the spread of projected global sea level rise dominates over internal variability. Internal variability, represented by ensemble spread, notably impacts the range of estuarine water levels and tidal current asymmetry in the shallow upper estuary. This area demonstrates large seasonal fluctuations of MWLs, the M2 tidal amplitude and monthly extreme water levels. On the monthly and inter-annual time scales, the MWL and M2 amplitude reveal opposite trends, indicative of a locally non-linear response to the decadal MSL rise enforced at the open boundary. Overall, imposed by the climate projections decadal change and MSL rise enhance the horizontal currents and turbulent diffusivities whereas internal variability locally mitigates sea level rise–driven changes in the water column. This work establishes a framework for providing consistent regionalised scenario-based climate change projections for the estuarine environment to support sustainable adaptation development.