How reanalysis helps study climate change

New paper alert! A fully coupled climate reanalysis by Vince Cooper covering 1850-2023. We used strongly coupled data assimilation on observations of sea surface temperature, land-based air temperature, sea-level pressure over the ocean, and satellite sea-ice concentration at monthly resolution. As far as we know, this is the first time that these fields have been simultaneously reconstructed over the historical period. Results show significant low-frequency variance in ENSO, with a peak near the start of the 20th century, muted modern cooling trends in Southern Ocean SST (see figure below), a decline in Arctic sea-ice area since the 19th century, and relatively small changes in Antarctic sea-ice area. Additional key points: * Most reanalysis datasets consider each component of the climate system independently (i.e., separate atmospheric and oceanic reanalyses), leading to inconsistencies in coupled variability. Here, we use strongly coupled data assimilation, which means that all observations update every component of the climate system. * Efficient emulators are used to propagate the memory of past observations forward in time. We use cyclostationary linear inverse models trained on 8 CMIP6 model simulations to include the role of model error in the reconstructions. These models are used to create 8 separate reanalyses, propagating the full error covariance matrix for all climate variables. * A 1600-member ensemble is created by sampling the posterior distributions in a dynamically consistent process, providing a large sample of equally likely reanalyses of historical climate. This provides a rich dataset for exploring climate variability with uncertainty quantification. The preprint can be found here: https://lnkd.in/gbEtR4Jw

🌍 Exciting news for climate researchers and enthusiasts! 🌦️ The ECMWF ERA5 reanalysis dataset is revolutionizing our understanding of global climate patterns. 🔄 ERA5, the fifth generation of ECMWF atmospheric reanalysis, combines cutting-edge model data with observations from around the world, creating a comprehensive and consistent dataset. It's a significant upgrade from its predecessor, ERA-Interim reanalysis. One of the remarkable offerings of ERA5 is the ERA5 DAILY dataset, which provides aggregated daily values for seven key climate parameters, including 2m air temperature, total precipitation, and wind components. Daily aggregates such as mean sea level pressure and surface pressure offer valuable insights into daily weather patterns. For researchers and data enthusiasts, ERA5 DAILY opens up avenues for exploring climate trends and understanding weather phenomena on a global scale. From tracking changes in precipitation patterns to studying wind dynamics, the possibilities are endless. 📊 And here's where the magic happens: utilizing tools like Google Earth Engine, we can harness the power of ERA5 data for localized analysis and visualization. Check out this code snippet using ERA5 DAILY to analyze precipitation patterns in the Ceará region of Brazil! See code bellow: // Defining the region of interest var gaul1 = ee.FeatureCollection("FAO/GAUL/2015/level1"); var brazilStates = gaul1.filter(ee.Filter.eq('ADM0_NAME', 'Brazil')); var roi = brazilStates.filter(ee.Filter.eq('ADM1_NAME', 'Ceara')); // Setting the study area Map.centerObject(roi); Map.addLayer(roi); // Setting the time interval var starting = '2010-01-01'; var ending = '2023-01-01'; // Applying unit conversion var eraPrec = ee.ImageCollection("ECMWF/ERA5_LAND/DAILY_AGGR") .filterDate(starting, ending) .filterBounds(roi); // Printing the collection print('Collection:', eraPrec); print('Number of images:', eraPrec.size()); // Function to convert m to mm and add property to the collection var Precipitation = function(img){ // Precipitation units are depth in meters: divide to get m / mm var bands = img.select('total_precipitation_sum').multiply(1000).clip(roi); return bands.rename('total_precipitation_sum') .set('date', img.date().format('YYYY-MM-dd')) .copyProperties(img,['system:time_start','system:time_end']); }; var eraPrecConverted = eraPrec.map(Precipitation); rest of the code down in the comments #ERA5 #ClimateData #ClimateResearch #DataScience #ECMWF #EarthObservation #ClimateChange #WeatherPatterns #GoogleEarthEngine #DataVisualization #Copernicus #ClimateAction #javascript #codetutorial #remotesensing

New research in Environmental Research: Climate probes how Arctic warming affects the Hadley circulation, which transports heat from the equator toward higher latitudes. The equator‑to‑pole temperature gradient drives the Hadley cell and mid‑latitude jet, yet Arctic amplification—the Arctic warming almost four times faster than the globe since 1979—is eroding that gradient. To explore the consequences, the authors analysed 1960‑2022 reanalysis data using empirical orthogonal functions of the meridional mass stream function. They identified two interannual modes: a first mode tied to climate teleconnections such as the Atlantic Multidecadal Oscillation, and a second mode that correlates 0.43 with the Arctic Amplification Index and strongly with Hadley cell strength and 200‑hPa meridional winds. Regression analysis reveals that both the AAI and this second mode share an increasing transient eddy momentum flux divergence (EMFD) in the subtropics. Enhanced EMFD weakens zonal winds and reduces baroclinicity, while momentum flux from mid‑latitudes triggers Rossby waves that reinforce the pattern. Sea‑ice experiments further show that Arctic warming increases EMFD and overturning, shifting the subtropical jet poleward. These eddy‑driven mechanisms mean Arctic warming is not just a polar issue but a driver of changes in global circulation. Continued warming could strengthen and shift the Hadley cell, influencing subtropical climate, jet‑stream behaviour and regional extremes. Understanding this dynamic coupling is essential for better climate projections and adaptation strategies. Source: https://lnkd.in/dJfbfc-v #ClimateScience #ArcticAmplification #HadleyCell #ClimateChange #AtmosphericScience #EnvironmentalResearch

The next climate breakthrough may not be a new turbine or battery. It may be a better baseline dataset. 🌍 Copernicus ECMWF has started ERA6 production — the next flagship climate reanalysis after ERA5. Why that matters is not the headline alone, but what sits underneath it: • 📍 14 km vs 31 km resolution means a much sharper view of storms, coastal dynamics, land–atmosphere interactions, and local weather risk. That matters for wind/solar siting, grid planning, insurance, and infrastructure resilience. • 🕒 Hourly reconstructions stretching back 75+ years create a stronger historical baseline for trend analysis, asset stress-testing, and climate-risk decisions that are still too often made on weak local proxies. • 🌊 First-time ocean–atmosphere coupling in ECMWF’s flagship reanalysis improves system coherence. That is a big deal for coastal risk, storm surge work, shipping, offshore wind, and any analysis where disconnected datasets distort reality. • 📚 +50% to more than doubling of some observations versus ERA5 is a reminder that climate intelligence is only as good as the data rescue, correction, and assimilation behind it. • 🤖 In the middle of the AI wave, ECMWF makes an important point: the best ML models still depend on robust physics-based training data. Better AI starts with better ground truth. My take: ⚡ We spend a lot of time talking about transition technologies, and not enough time talking about measurement infrastructure. But better data changes real money decisions: where to build, how to insure, what to finance, how to model risk, and which assets stay competitive under climate stress. For energy, transport, and physical-risk teams, ERA6 is not “just another dataset”. It is an upgrade to the decision layer. 📈 Watch next: 👀 • Late 2027: first decades of ERA6 data expected • Early 2028: first four decades expected to be downloadable Who will benefit first from ERA6-quality data? A) renewables & grids B) insurers & infrastructure investors C) logistics / shipping / coastal risk teams #ClimateData #Copernicus #ECMWF #EnergyTransition #ClimateRisk #Infrastructure #AI