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Alberto Troccoli
,
Tobias Fuchs
,
Roberta Boscolo
,
Elah Matt
, and
Hamid Bastani

Abstract

Weather and Climate Services (W&CS) are key to supporting the transition to net-zero emissions in the energy sector. These services are utilised to increase energy system resilience, enhance renewable energy deployment, and enable uptake of energy-efficiency measures and innovations. As energy systems become increasingly dependent on and affected by weather and climatic conditions, integrating weather and climate data into energy management systems is essential.

This paper addresses the gap in comprehensive guidance for developing integrated W&CS to support net-zero energy transitions, drawing upon a report by the World Meteorological Organization’s Services Commission Study Group on Integrated Energy Services (WMO 2023). We present a framework for co-production of W&CS, exploring how the uptake of W&CS for energy transitions can be enabled through evaluation of socio-economic benefits, harnessing business models, identification of key policies, and capacity development measures.

To support the uptake of W&CS for net-zero energy transitions we recommend: a deeper understanding of user needs and requirements; continuous advancements in the science and technology of W&CS; effective integration of weather and climate data within energy conversion models; improved accessibility and sharing of meteorological and, especially, energy data; promotion of co-production approaches; exploration of novel applications of W&CS in the energy sector; refinement of business models for sustainable W&CS delivery; execution of capacity-building activities; enhanced communication among stakeholders and strengthened collaborative efforts. These steps are crucial for realizing the full potential of W&CS in driving the energy sector towards a sustainable, net-zero future.

Open access
E. Katragkou
,
S. P. Sobolowski
,
C. Teichmann
,
F. Solmon
,
V. Pavlidis
,
D. Rechid
,
P. Hoffmann
,
J. Fernandez
,
G. Nikulin
, and
D. Jacob

Abstract

The Coordinated Regional Downscaling Experiment (CORDEX) is a coordinated international activity that has produced ensembles of regional climate simulations with domains that cover all land areas of the world. These ensembles are used by a wide range of practitioners that include the scientific community, policymakers, and stakeholders from the public and private sectors. They also provide the scientific basis for the Intergovernmental Panel on Climate Change-Assessment Reports. As its next phase now launches, the CMIP6-CORDEX datasets are expected to populate community repositories over the next couple of years, with updated state-of-the-art regional climate data that will further support national and regional communities and inform their climate adaptation and mitigation strategies. The protocol presented here focuses on the European domain (EURO-CORDEX). It takes the international CORDEX protocol covering all 14 global domains as its template. However, it expands on the international protocol in specific areas; incorporates historical and projected aerosol trends into the regional models in a consistent way with CMIP6 global climate models, to allow for a better comparison of global versus regional trends; produces more climate variables to better support sectorial climate impact assessments; and takes into account the recent scientific developments addressed in the CORDEX Flagship Pilot Studies, enabling a better assessment of processes and phenomena relevant to regional climate (e.g., land-use change, aerosol, convection, and urban environment). Here, we summarize the scientific analysis which led to the new simulation protocol and highlight the improvements we expect in the new generation regional climate ensemble.

Open access
Detlef Stammer
,
Daniel E. Amrhein
,
Magdalena Alonso Balmaseda
,
Laurent Bertino
,
Massimo Bonavita
,
Carlo Buontempo
,
Nico Caltabiano
,
Francois Counillon
,
Ian Fenty
,
Raffaele Ferrari
,
Yosuke Fujii
,
Shreyas Sunil Gaikwad
,
Pierre Gentine
,
Andrew Gettelman
,
Ganesh Gopalakrishnan
,
Patrick Heimbach
,
Hans Hersbach
,
Chris Hill
,
Shinya Kobayashi
,
Armin Köhl
,
Paul J. Kushner
,
Matthew Mazloff
,
Hisashi Nakamura
,
Stephen G. Penny
,
Laura Slivinski
,
Susann Tegtmeier
, and
Laure Zanna
Open access
Emiel van der Plas
,
Aart Overeem
,
Jan Fokke Meirink
,
Hidde Leijnse
, and
Linda Bogerd

Abstract

A new pan-European climatological dataset was recently released that has a much higher spatiotemporal resolution than existing pan-European interpolated rain gauge datasets. This radar dataset of hourly precipitation accumulations, EURADCLIM (Overeem et al. 2023), covers most of continental Europe with a resolution of 2 km × 2 km, and is adjusted employing data from potentially thousands of government rain gauges. This study aims to use this dataset to evaluate two important satellite-derived precipitation products over the period 2013 to 2019 at a much higher spatiotemporal resolution than was previously possible at the European scale: the IMERG late run and the Meteosat Second Generation (MSG) Cloud Physical Properties product from the SEVIRI instrument. The latter is only available during daytime, so the analyses are restricted to daytime conditions. A direct grid cell comparison of hourly precipitation reveals an apparently low coefficient of correlation. However, looking into slightly more detail at statistics pertaining to longer time scales or specific areas, the datasets show good correspondence. All datasets are shown to have their specific biases, that can be transient or more systematic, depending on the timing or location. The MSG precipitation seems to have an overall positive bias and the IMERG dataset suffers from some transient overestimation of certain events.

Restricted access
Jiali Wang
,
Georgios Deskos
,
William J. Pringle
,
Sue Ellen Haupt
,
Sha Feng
,
Larry K. Berg
,
Matt Churchfield
,
Mrinal Biswas
,
Walter Musial
,
Paytsar Muradyan
,
Eric Hendricks
,
Rao Kotamarthi
,
Pengfei Xue
,
Christopher M. Rozoff
, and
George Bryan
Open access
Chin-Hsuan Peng
and
Xingchao Chen

Abstract

Previous observational studies have indicated that mesoscale convective systems (MCSs) contribute the majority of precipitation over the Bay of Bengal (BoB) during the summer monsoon season, yet their initiation and propagation remain incompletely understood. To fill this knowledge gap, we conducted a comprehensive study using a combination of 20-year satellite observations, MCS tracking, reanalysis data, and a theoretical linear model. Satellite observations reveal clear diurnal propagation signals of MCS initiation frequency and rainfall from the west coast of the BoB toward the central BoB, with the MCS rainfall propagating slightly slower than the MCS initiation frequency. Global reanalysis data indicates a strong association between the offshore-propagating MCS initiation frequency/rainfall and diurnal low-level wind perturbations, implying the potential role of gravity waves. To verify the hypothesis, we developed a 2-D linear model that can be driven by realistic meteorological fields from reanalysis. The linear model realistically reproduces the characteristics of offshore-propagating diurnal wind perturbations. The wind perturbations, as well as the offshore propagation signals of MCS initiation frequency and rainfall, are associated with diurnal gravity waves emitted from the coastal regions, which in turn are caused by the diurnal land-sea thermal contrast. The ambient wind speed and vertical wind shear play crucial roles in modulating the timing, propagation, and amplitude of diurnal gravity waves. Using the linear model and satellite observations, we further show that the stronger monsoonal flows lead to faster offshore propagation of diurnal gravity waves, which subsequently control the offshore propagation signals of MCS initiation and rainfall.

Restricted access
Yunyao Li
,
Daniel Tong
,
Peewara Makkaroon
,
Timothy DelSole
,
Youhua Tang
,
Patrick Campbell
,
Barry Baker
,
Mark Cohen
,
Anton Darmenov
,
Ravan Ahmadov
,
Eric James
,
Edward Hyer
, and
Peng Xian

Abstract

Wildfires pose increasing risks to human health and properties in North America. Due to large uncertainties in fire emission, transport, and chemical transformation, it remains challenging to accurately predict air quality during wildfire events, hindering our collective capability to issue effective early warnings to protect public health and welfare. Here, we present a new real-time Hazardous Air Quality Ensemble System (HAQES) by leveraging various wildfire smoke forecasts from three U.S. federal agencies (NOAA, NASA, and Navy). Compared to individual models, the HAQES ensemble forecast significantly enhances forecast accuracy. To further enhance forecasting performance, a weighted ensemble forecast approach was introduced and tested. Compared to the unweighted ensemble mean, the multilinear regression weighted ensemble reduced fractional bias by 34% in the major fire regions, false alarm rate by 72%, and increased hit rate by 17%. Finally, we improved the weighted ensemble using quantile regression and weighted regression methods to enhance the forecast of extreme air quality events. The advanced weighted ensemble increased the PM2.5 exceedance hit rate by 55% compared to the ensemble mean. Our findings provide insights into the development of advanced ensemble forecast methods for wildfire air quality, offering a practical way to enhance decision-making support to protect public health.

Open access
Pep Cos
,
Raül Marcos-Matamoros
,
Markus Donat
,
Rashed Mahmood
, and
Francisco J. Doblas-Reyes

Abstract

There are several methods to constrain multi-model projections of future climate. This study assesses the quality of four constraining methods in representing the near-term summer temperature projections of the Mediterranean region. Three are based on phasing in ocean surface temperature variations based on observations or decadal predictions, and method is based on measuring performance and independence of the individual simulations. The comparison has been carried out with a new framework inspired by the forecast quality assessment of decadal predictions. The framework led to quality estimates of the constrained projection approaches obtained by producing 20-year temperature estimates every year from 1970 to 2000 and computing quality metrics against observational references.

The evaluation results show some differences between constraining approaches. The improvement or deterioration against quality measures of the full, unconstrained, CMIP6 ensemble show strong spatial heterogeneity. From the analysis of the selection approaches it is found that the constraints based on sea surface temperature (SST) fields are affected not only by the variability but also by the warming trend. The weighting method generally shows small quality differences with respect to the full CMIP6 ensemble. Despite caveats of the different methods there is potential to improve the near-term climate projections as some significant quality enhancements were found in some approaches according to the evaluation metrics used. This study suggests a good understanding of the constraining methods and their forecast quality is required before using them to take informed decisions. Our study opens the door to optimising these methods for the Mediterranean climate and highlights the need for evaluating the constraints through retrospective assessments against observational references.

Restricted access
Karen A. Kosiba
,
Anthony W. Lyza
,
Robert J. Trapp
,
Erik N. Rasmussen
,
Matthew Parker
,
Michael I. Biggerstaff
,
Stephen W. Nesbitt
,
Christopher C. Weiss
,
Joshua Wurman
,
Kevin R. Knupp
,
Brice Coffer
,
Vanna C. Chmielewski
,
Daniel T. Dawson
,
Eric Bruning
,
Tyler M. Bell
,
Michael C. Coniglio
,
Todd A. Murphy
,
Michael French
,
Leanne Blind-Doskocil
,
Anthony E. Reinhart
,
dward Wolff
,
Morgan E. Schneider
,
Miranda Silcott
,
Elizabeth Smith
,
oshua Aikins
,
Melissa Wagner
,
Paul Robinson
,
James M. Wilczak
,
Trevor White
,
David Bodine
,
Matthew R. Kumjian
,
Sean M. Waugh
,
A. Addison Alford
,
Kim Elmore
,
Pavlos Kollias
, and
David D. Turner

Abstract

Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the U.S., but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear System (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned two years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.

Open access
Benjamin Le Roy
,
Aude Lemonsu
,
Robert Schoetter
, and
Tiago Machado

Abstract

High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical-dynamical downscaling approach (SDD) is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970-2099, while maintaining the city’s land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum(maximum) temperatures (TN/TX) that are too high(low). After correction and downscaling, the urban simulations inherit some of these biases, but give satisfactory results for summer urban heat islands (UHI), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 K and 4.8 K for TN and TX for RCP8.5. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 K to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content, and are partially offset by increased ground heat storage in the city.

Restricted access