The El Niño Southern Oscillation (ENSO) is the most important driver of interannual global climate variability and can trigger extreme weather events and disasters in various parts of the globe. Recently, we have developed two approaches for the early forecasting of El Niño. The climate network-based approach allows forecasting the onset of an El Niño event about 1 year ahead. The complexity-based approach allows additionally to forecast the magnitude of an upcoming El Niño event in the calendar year before. Here we communicate the forecasts of both methods for 2022.
We argue that it would be desirable to use Jeffreys' priors in the construction of numerical model based probabilistic climate forecasts, in order that those forecasts could be argued to be objective. Hitherto, this has been considered computationally unfeasible. We propose an approximation that we believe makes it feasible, and derive closed-form expressions for various simple cases.
When climate forecasts are highly uncertain, the optimal mean squared error strategy is to ignore them. When climate forecasts are highly certain, the optimal mean squared error strategy is to use them as is. In between these two extremes there are climate forecasts with an intermediate level of uncertainty for which the optimal mean squared error strategy is to make a compromise forecast. We present two new methods for making such compromise forecasts, and show, using simulations, that they improve on previously published methods.
Uncertainty around multimodel ensemble forecasts of changes in future climate reduces the accuracy of those forecasts. For very uncertain forecasts this effect may mean that the forecasts should not be used. We investigate the use of the well-known Bayesian Information Criterion (BIC) to make the decision as to whether a forecast should be used or ignored.
Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: How good are seasonal climate forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal climate forecasts are made from ensembles of integrations of numerical models of climate. We argue that goodness should be assessed primarily in terms of the probabilistic reliability of these ensemble-based forecasts and that a 5 should be reserved for systems which are not only reliable overall, but where, in particu…
We describe a simple method that utilises the standard idea of bias-variance trade-off to improve the expected accuracy of numerical model forecasts of future climate. The method can be thought of as an optimal multi-model combination between the forecast from a numerical model multi-model ensemble, on one hand, and a simple statistical forecast, on the other. We apply the method to predictions for UK temperature and precipitation for the period 2010 to 2100. The temperature predictions hardly change, while the precipitation predictions show large changes.
Climate network and complexity based ENSO forecast for 2026
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association（波茨坦气候影响研究所（PIK），莱布尼茨协会成员）；State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所地球系统数值模拟与应用国家重点实验室）；School of Systems Science, Beijing Normal University（北京师范大学系统科学学院）；Institute for Theoretical Physics, Justus Liebig University Giessen（吉森大学约翰内斯·古腾堡理论物理研究所）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）
To include parameter uncertainty into probabilistic climate forecasts one must first specify a prior. We advocate the use of objective priors, and, in particular, the Jeffreys' Prior. In previous work we have derived expressions for the Jeffreys' Prior for the case in which the observations are independent and normally distributed. These expressions make the calculation of the prior much simpler than evaluation directly from the definition. In this paper, we now relax the independence assumption and derive expressions for the Jeffreys' Prior for the case in which the observations are distribut…
Virginia Tech（弗吉尼亚理工大学）；IBM Research Africa（IBM非洲研究院）；IBM Research Europe（IBM欧洲研究院）
As acute climate change impacts weather and climate variability, there is increased demand for robust climate impact model predictions from which forecasts of the impacts can be derived. The quality of those predictions are limited by the climate drivers for the impact models which are nonlinear and highly variable in nature. One way to estimate the uncertainty of the model drivers is to assess the distribution of ensembles of climate forecasts. To capture the uncertainty in the impact model outputs associated with the distribution of the input climate forecasts, each individual forecast ensem…
The regional climate model prepared from Eta WS forecast model has been integrated over South America with the horizontal resolution of 40 km for the period of 1960-1990. The model was forced at its lateral boundaries by the outputs of HadAM3P. The data of HadAM3P represent simulation of modern climate with the resolution about 150 km. In order to prepare climate regional model from the Eta forecast model multiple modifications and corrections were made in the original model. The run of climate Eta model was made on the supercomputer SX-6. The detailed analysis of the results of dynamical down…
Sensitivity of the ECMWF seasonal forecast model to CO2 and anthropogenic aerosol forcings: Experimental design and impact on climate trends
ECMWF（欧洲中期天气预报中心）；Department of Meteorology and Geophysics, University of Vienna（维也纳大学气象与地球物理系）；NCAS, University of Oxford, Atmospheric, Oceanic and Planetary Physics（牛津大学大气、海洋和行星物理国家环境科学研究中心）
In this paper, we assess and develop a climate service focused on the production of seasonal predictions for summer wildfires in a Mediterranean region through a participatory approach with end-users. We start by building a data-driven model that links a drought indicator (Standardised Precipitation Evapotranspiration Index; SPEI) with a series of burned areas in Catalonia (northeastern Spain). Afterwards, we feed this model with SPEI forecasts obtained through a combination of the antecedent observed conditions and climatology. Finally, we assess the forecasting skill of the system by using c…
The two main drivers of climate change on sub-Milankovic time scales are re-assessed by means of a multiple regression analysis. Evaluating linear combinations of the logarithm of carbon dioxide concentration and the geomagnetic aa-index as a proxy for solar activity, we reproduce the sea surface temperature (HadSST) since the middle of the 19th century with an adjusted $R^2$ value of around 87 per cent for a climate sensitivity (of TCR type) in the range of 0.6 K until 1.6 K per doubling of CO$_2$. The solution of the regression is quite sensitive: when including data from the last decade, th…
University of Minnesota, Twin Cities（明尼苏达大学双城校区）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；George Mason University（乔治梅森大学）
Sub-seasonal climate forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to ad…
The climate version Eta CCS, prepared from the NCEP Eta forecast model, was integrated over South America for the period from January 1979 to December 1983. The model was driven by the two sets of boundary conditions derived from the reanalysis and outputs of HadAM3P atmospheric global model. The mean output fields of precipitation, precipitation frequency, and near surface air temperature, simulated by the Eta model, were compared with the observational data of the CRU and GPCP projects. This comparison shows that the Eta model reproduces well the main patterns of the summer and winter observ…
Two future scenarios that are not explicitly in the range of scenarios (the Representative Concentration Pathway scenarios) utilised by the IPCC. These two scenarios are the emissions trend under peak fossil fuel (for example, Mohr et al., 2015); and the climate sensitivity determinable from the relationship between first-difference CO2 and temperature recently shown by Leggett and Ball (2015). This paper provides forecasts of a global surface temperature trajectory to 2100 resulting from the effect of these two scenarios. The time-series models developed both displayed high statistical signif…
Univ Lyon, Ens de Lyon, Univ Claude Bernard, CNRS, Laboratoire de Physique（里昂大学、里昂高等师范学院、克洛德·贝尔纳大学、法国国家科学研究中心、物理实验室）；Royal Meteorological Institute（皇家气象研究所）
Because of the impact of extreme heat waves and heat domes on society and biodiversity, their study is a key challenge. We specifically study long-lasting extreme heat waves, which are among the most important for climate impacts. Physics driven weather forecast systems or climate models can be used to forecast their occurrence or predict their probability. The present work explores the use of deep learning architectures, trained using outputs of a climate model, as an alternative strategy to forecast the occurrence of extreme long-lasting heatwaves. This new approach will be useful for severa…
The Indian summer monsoon rainfall (ISMR) has a decisive influence on India's agricultural output and economy. Extreme deviations from the normal seasonal amount of rainfall can cause severe droughts or floods, affecting Indian food production and security. Despite the development of sophisticated statistical and dynamical climate models, a long-term and reliable prediction of the ISMR has remained a challenging problem. Towards achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near surface air temperature field. We uncover that some…
Neural General Circulation Models for Weather and Climate
Google Research（谷歌研究院）；Massachusetts Institute of Technology（麻省理工学院）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；Google DeepMind（谷歌DeepMind）；Harvard University（哈佛大学）
Ensemble forecasting is a technique devised to palliate sensitivity to initial conditions in nonlinear dynamical systems. The basic idea to avoid this sensitivity is to run the model many times under several slightly-different initial conditions, merging the resulting forecast in a combined product. We argue that this blending procedure is unphysical, and that a single trajectory should be chosen instead. We illustrate our case with a climate model. While most of the current climate simulations use the ensemble average technique as merging procedure, this paper shows that this choice presents …
University of Hawaii Manoa（夏威夷大学马诺阿分校）；Nimbus AI LLC（Nimbus人工智能有限责任公司）
Forecasting future weather and climate is inherently difficult. Machine learning offers new approaches to increase the accuracy and computational efficiency of forecasts, but current methods are unable to accurately model uncertainty in high-dimensional predictions. Score-based diffusion models offer a new approach to modeling probability distributions over many dependent variables, and in this work, we demonstrate how they provide probabilistic forecasts of weather and climate variables at unprecedented resolution, speed, and accuracy. We apply the technique to day-ahead solar irradiance fore…
Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss
The Alan Turing Institute（艾伦·图灵研究所）；Department of Cyrospheric and Atmospheric Sciences, University of Innsbruck（因斯布鲁克大学冰冻圈与大气科学系）；British Antarctic Survey（英国南极调查局）；Autodesk Research（欧特克研究院）
Climate exhibits a vast range of dissipative structures. Some have characteristic times of a few days; others evolve on thousands of years. All these structures are interdependent; in other words, they communicate. It is often considered that the only way to cope with climate complexity is to integrate the equations of atmospheric and oceanic motion with the finer possible mesh. Is this the sole strategy? Aren't we missing another characteristic of the climate system: its ability to destroy and generate information at the macroscopic scale? Paleoclimatologists consider that much of this inform…
The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, …
El Niño is a typical example of a coupled atmosphere--ocean phenomenon, but it is unclear whether it can be described quantitatively by a correlation between relevant climate events. To provide clarity on this issue, we developed a machine learning-based El Niño prediction model that uses the time series of climate indices. By transforming the multidimensional time series into the path signature, the model is able to properly evaluate the order and nonlinearity of climate events, which allowed us to achieve good forecasting skill (mean square error = 0.596 for 6-month prediction). In addition,…
The El Niño Southern Oscillation (ENSO) is the most important driver of interannual global climate variability and can trigger extreme weather events and disasters in various parts of the globe. Depending on the region of maximal warming, El Niño events can be partitioned into 2 types, Eastern Pacific (EP) and Central Pacific (CP) events. The type of an El Niño has a major influence on its impact and can even lead to either dry or wet conditions in the same areas on the globe. Here we show that the zonal difference $ΔT_{WP-CP}$ between the sea surface temperature anomalies (SSTA) in the equato…
Massachusetts Institute of Technology（麻省理工学院）；Argonne National Laboratory（阿贡国家实验室）
Accurately forecasting the weather is a key requirement for climate change mitigation. Data-driven methods offer the ability to make more accurate forecasts, but lack interpretability and can be expensive to train and deploy if models are not carefully developed. Here, we make use of two historical climate data sets and tools from machine learning, to accurately predict temperature fields. Furthermore, we are able to use low fidelity models that are cheap to train and evaluate, to selectively avoid expensive high fidelity function evaluations, as well as uncover seasonal variations in predicti…
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly resolve processes larger than 100 km and approximate any process occurring below this scale (e.g. thunderstorms) using so-called parametrizations. Machine learning could improve upon the accuracy of some traditional physical parametrizations by learning from so-called global cloud-resolving models. We compare the performance of two machine learning models, random forests (RF) and neural networks (NNs), at parametrizin…
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates（Sorbonne大学科学与工程系）；Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India（印度统计研究院应用数学与物理单元）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, 75005, France（Sorbonne大学人工智能中心）；Indian Institute of Technology Gandhinagar, Gandhinagar 382355, India（印度理工学院冈丁加尔分校）；National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Vasco da Gama, 403804, India（国家极地与海洋研究中心）；Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad-500032, India（国际信息技术研究所计算自然科学与生物信息中心）；Department of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90–537 Lodz, Poland（Lodz技术大学动力学系）；Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India（贾瓦德普大学数学生物学与生态中心）
Understanding seasonal climatic conditions is critical for better management of resources such as water, energy and agriculture. Recently, there has been a great interest in utilizing the power of artificial intelligence methods in climate studies. This paper presents a cutting-edge deep learning model (UNet++) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for finetuning as well performance analysis in the validation dataset. Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + el…
Some issues which are relevant for the recent state in climate modeling have been considered. A detailed overview of literature related to this subject is given. The concept in modeling of climate, as a complex system, seen through Godel's Theorem and Rosen's definition of complexity and predictability is discussed. It is pointed out to occurrence of chaos in computing the environmental interface temperature from the energy balance equation given in a difference form. A coupled system of equations, often used in climate models is analyzed. It is shown that the Lyapunov exponent mostly has posi…
The last decade has seen the success of stochastic parameterizations in short-term, medium-range and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy and improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing longstanding climate biases and relevant for determining the climate response to external forcing. This a…
