Month: September 2024

Abstract

The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements.

Abstract

In the context of changes in global climate and land uses, biodiversity patterns and plant species distributions have been significantly affected. Soil salinization is a growing problem, particularly in the arid areas of Northwest China. Halophytes are ideal for restoring soil salinization because of their adaptability to salt stress. In this study, we collected the current and future bioclimatic data released by the WorldClim database, along with soil data from the Harmonized World Soil Database (v1.2) and A Big Earth Data Platform for Three Poles. Using the maximum entropy (MaxEnt) model, the potential suitable habitats of six halophytic plant species (Halostachys caspica (Bieb.) C. A. Mey., Halogeton glomeratus (Bieb.) C. A. Mey., Kalidium foliatum (Pall.) Moq., Halocnemum strobilaceum (Pall.) Bieb., Salicornia europaea L., and Suaeda salsa (L.) Pall.) were assessed under the current climate conditions (average for 1970–2000) and future (2050s, 2070s, and 2090s) climate scenarios (SSP245 and SSP585, where SSP is the Shared Socio-economic Pathway). The results revealed that all six halophytic plant species exhibited the area under the receiver operating characteristic curve values higher than 0.80 based on the MaxEnt model, indicating the excellent performance of the MaxEnt model. The suitability of the six halophytic plant species significantly varied across regions in the arid areas of Northwest China. Under different future climate change scenarios, the suitable habitat areas for the six halophytic plant species are expected to increase or decrease to varying degrees. As global warming progresses, the suitable habitat areas of K. foliatum, S. salsa, and H. strobilaceum exhibited an increasing trend. In contrast, the suitable habitat areas of H. glomeratus, S. europaea, and H. caspica showed an opposite trend. Furthermore, considering the ongoing global warming trend, the centroids of the suitable habitat areas for various halophytic plant species would migrate to different degrees, and four halophytic plant species, namely, S. salsa, H. strobilaceum, H. gbmeratus, and H. capsica, would migrate to higher latitudes. Temperature, precipitation, and soil factors affected the possible distribution ranges of these six halophytic plant species. Among them, precipitation seasonality (coefficient of variation), precipitation of the warmest quarter, mean temperature of the warmest quarter, and exchangeable Na⁺ significantly affected the distribution of halophytic plant species. Our findings are critical to comprehending and predicting the impact of climate change on ecosystems. The findings of this study hold significant theoretical and practical implications for the management of soil salinization and for the utilization, protection, and management of halophytes in the arid areas of Northwest China.

Abstract

The European North-West shelf seas (NWS) support economic interests and provide environmental services to adjacent countries. Expansion of offshore activities, such as renewable energy infrastructure, aquaculture, and growth of international shipping, will place increasingly complex demands on the marine environment over the coming decades. Skilful forecasting of NWS properties on seasonal timescales will help to effectively manage these activities. Here we quantify the skill of an operational large-ensemble ocean-atmosphere coupled global forecasting system (GloSea), as well as benchmark persistence forecasts, for predictions of NWS sea surface temperature (SST) at 2–4 months lead time in winter and summer. We identify sources of and limits to SST predictability, considering what additional skill may be available in the future. We find that GloSea NWS SST skill is generally high in winter and low in summer. GloSea outperforms simple persistence forecasts by adding information about atmospheric variability, but only to a modest extent as persistence of anomalies in the initial conditions contributes substantially to predictability. Where persistence is low – for example in seasonally stratified regions – GloSea forecasts show lower skill. GloSea skill can be degraded by model deficiencies in the relatively coarse global ocean component, which lacks dynamic tides and subsequently fails to robustly represent local circulation and mixing. However, “atmospheric mode matched” tests show potential for improving prediction skill of currently low performing regions if atmospheric circulation forecasts can be improved. This underlines the importance of coupled atmosphere-ocean model development for NWS seasonal forecasting applications.

Abstract

The Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) was developed and implemented operationally at Environment and Climate Change Canada (ECCC) to support a variety of critical marine applications. These include support for ice services, search and rescue, environmental emergency response and maritime safety. CIOPS-E uses a 1/36° horizontal grid (~ 2 km) to simulate sea ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). Forcing at lateral open boundaries is taken from ECCC’s data assimilative Regional Ice-Ocean Prediction System (RIOPS). A spectral nudging method is applied offshore to keep mesoscale features consistent with RIOPS. Over the continental shelf and GSL, the CIOPS-E solution is free to evolve according to the model dynamics. Overall, CIOPS-E significantly improves the representation of tidal and sub-tidal water levels compared to ECCC’s lower resolution systems: RIOPS (~ 6 km) and the Regional Marine Prediction System – GSL (RMPS-GSL, 5 km). Improvements in the GSL are due to the higher resolution and a better representation of bathymetry, boundary forcing and dynamics in the upper St. Lawrence Estuary. Sea surface temperatures show persistent summertime cold bias, larger in CIOPS-E than in RIOPS, as the latter is constrained by observations. The seasonal cycle of sea ice extent and volume, unconstrained in CIOPS-E, compares well with observational estimates, RIOPS and RMPS-GSL. A greater number of fine-scale features are found in CIOPS-E with narrow leads and more intense ice convergence zones, compared to both RIOPS and RMPS-GSL.