Andrew W. Robertson

• Columbia University

This is the supplementary information of the paper "A climatology of local hourly wet spells across the tropics" in Climate Dynamics (DOI:https://doi.org/10.1007/s00382-025-07714-8)

We explore the relationships amongst duration, total amount, mean and maximum hourly intensity of rainfall and spatial scale for more than a thousand rain gauges covering a diverse range of tropical and subtropical (30°N-30°S) climates, from arid (< = 200 mm year⁻¹) to very wet (> 3000–4000 mm year⁻¹). We find that the interannual variation of seas...

This paper provides an updated assessment of the "International Research Institute for Climate and Society's (IRI) El Niño Southern Oscillation (ENSO) Predictions Plume". We evaluate 253 real-time forecasts of the Niño 3.4 index issued from February 2002 to February 2023 and examine multimodal means of dynamical (DYN) and statistical (STAT) models...

The sub-seasonal characteristics of tropical rainfall, such as rainfall frequency and mean intensity, occurrence and length of wet and dry spells, timing of onset and withdrawal of the wet season, etc. are important applications-relevant targets for sub-seasonal to seasonal (S2S) forecasts. The relationship between S2S prediction skill, potential p...

We explore the relationships amongst duration, total amount, mean and maximum hourly intensity of rainfall and spatial scale for more than a thousand rain gauges covering a diverse range of tropical and subtropical (30°N-30°S) climates, from arid (<= 200 mm year⁻¹) to very wet (> 3000-4000 mm year⁻¹). We find the interannual variation of seasonal (...

In 2022, a record-breaking monsoon caused flooding throughout Pakistan, particularly in the southern regions, resulting in deaths, property losses, and severe crop damage, affecting the food supply chain that could last for years. This study assesses the accuracy of sub-seasonal calibrated probabilistic rainfall forecasts for Pakistan. The evaluati...

This paper provides an updated assessment of the “International Research Institute for Climate and Society's (IRI) El Niño Southern Oscillation (ENSO) Predictions Plume". We evaluate 247 real-time forecasts of the Niño 3.4 index from February 2002 to August 2022 and examine multimodal means of dynamical (DYN) and statistical (STAT) models separatel...

California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022-2023. Following three years of drought from 2020-2022, intense landfalling ARs across California in December 2022 – January 2023 were responsible for bringing reservoirs back to historical averages and producing dam...

In South Asia (SA), the boreal summer monsoon (June to September; JJAS) and the El Niño-Southern Oscillation (ENSO) are connected, though different areas in SA respond differently to ENSO. In this paper, a new 41-year (1981 to 2021) high-resolution gridded rainfall dataset (ENACTS-BMD; Enhancing National Climate Services for Bangladesh Meteorologic...

A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal Experiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of e...

We describe an innovative forecast presentation that aims to overcome obstacles to using seasonal climate forecasts for decision making, trace factors that influenced how seasonal forecast conventions have evolved, and describe a workshop process for training and supporting farmers in sub-Saharan Africa to use probabilistic seasonal forecasts. Main...

This paper assesses the skill of the Saudi-King Abdulaziz University coupled ocean–atmosphere Global Climate Model, namely Saudi-KAU CGCM, in forecasting the El Niño-Southern Oscillation (ENSO)-related sea surface temperature. The model performance is evaluated based on a reforecast of 38 years from 1982 to 2019, with 20 ensemble members of 12-mont...

This study evaluates the ability of state-of-the-art subseasonal to seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden Julian Oscillations and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostic...

Predictability of Ethiopian Kiremt rainfall (June to September: JJAS) and forecast skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system 5 (SEAS5) is explored during 1981–2019. The first empirical orthogonal function of observed rainfall explains 50.6% of the total variability and is chara...

The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge...

Skillful seasonal climate forecasts can support decision making in water resources management and agricultural planning. In arid and semi‐arid regions, tailoring reliable forecasts has the potential to improve water management by using key hydroclimate variables months in advance. This article analyses and compares the performance of two common app...

Subseasonal to seasonal (S2S) tropical rainfall predictability is assessed both from an analysis of the spatial scales of observed rainfall variability data, as well as from an S2S model reforecast skill. Observed spatial scales are quantified from gridded observed daily rainfall data, in terms of the size (area) of daily contiguous wet grid‐points...

This talk establishes a link between duration of local-scale wet events (defined from hourly rainfall records), area covered by rainfall (from IMERG data) and predictability (using a S2S ensemble from ECMWF) across India. Larger and longer wet events are more predictable than smaller and shorter ones. But shorter events still convey a significant a...

This talk compares some characteristics of daily rainfall (as mean daily intensity, wet patches area, spatial scales of rainfall anomalies, amplitude of variance conveyed by specific bandwidths) to skill and reproducibility at S2S timescales. It gives also details about S2S predictability across India.

Faced with the greatest public health crisis of our time, people must work together and learn from each other to overcome the complex challenges facing our communities, countries, and the world. Climate-related hazards are one of those challenges; they exacerbate already challenging public health conditions and impact not just people, but also the...

The general public is familiar with weather forecasts and their utility, and the field of weather forecasting is well-established. Even the theoretical limit of the weather forecasting – two weeks – is known. In contrast, familiarity with climate prediction is low outside of the research field, the theoretical basis is not fully established, and we...

Recent research has highlighted the potential for improving predictive skill at the subseasonal timescale, which could be the basis for enhanced, actionable forecasts for climate services involving water and disaster management, health, energy and food security. Projects such as WMOʼs World Weather and World Climate Research Programmeʼs Subseasonal...

The Columbia University World Project “Adapting Agriculture to Climate Today, for Tomorrow” (ACToday), led by the International Research Institute for Climate and Society (IRI), was launched at the end of 2017 to create climate service solutions to help end hunger, achieve food security, improve nutrition, and promote sustainable agriculture in six...

Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between daily weather forecasts and seasonal climate outlooks. Recent scientific advances have identified sources of predictability on this time range, and modeling advances are leading to better forecasts. However, much remains to...

Large-scale atmospheric circulation regime structures are used to diagnose subseasonal forecasts of wintertime geopotential height fields over the North American sector, from the NCEP CFSv2 model. Four large-scale daily circulation regimes derived from reanalysis 500hPa geopotential height data using K-means clustering are used as a low-dimensional...

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve...

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather f...

Calibrated probabilistic forecasts of weekly rainfall were developed for the state of Bihar in northern India and issued in real time during the June–September 2018 monsoon period, up to 2 weeks in advance. The forecasts are based on subseasonal forecasts from the U.S. National Centers for Environmental Prediction CFSv2 model and were calibrated ag...

Tropical rainfall is mostly convective and its subseasonal-to-seasonal (S2S) prediction remains challenging. We show that state-of-art model forecast skill 3-4 weeks ahead is systematically lower over land than ocean, which is matched by a similar land-ocean contrast in the spatial scales of observed biweekly rainfall anomalies. Regional difference...

This is the supplementary information of the main text in press in NPJ Atmospheric and Climate Science

This matlab dataset contains the onset and withdrawal dates of the boreal summer monsoon as explained in the paper. The onset dates are available in the matrix "Ons2" and the withdrawal dates are available in the matrix "End2". All dates refer to Jan 1st of the years 1901-2014 for 4954 grid-points defined by the LON/LAT vectors.

Successful climate services often involve the use of tailored regional climate forecasts at one or multiple timescales. The way those forecasts are implemented is not always straightforward, and depends on several different factors, like which variables, models and calibration methods to use, how to produce the ensemble and tailoring, or even how t...

The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the...

This paper reviews research done by the authors and their collaborators at IRI and beyond over the past decade on predictability and prediction of Indian summer monsoon rainfall (ISMR) on seasonal and sub-seasonal timescales. Empirical analyses of the daily ISMR characteristics at local scales pertinent to agriculture, based on IMD gridded data, re...

Considering lessons learned from experiences at seasonal timescale, this talk discusses some concrete S2S applications using both calibrated and uncalibrated forecasts from the S2S Database and the SubX project. First, we illustrate how a Python interface for IRI’s Climate Predictability Tool —PyCPT— can be employed to assess skill and calibrate su...

Intraseasonal timescales, more recently called sub-seasonal to seasonal (S2S), range from the deterministic limit of atmospheric predictability (about 10 days) up to a season (say, 100 days). These timescales occupy a window of overlap between low-frequency variability (LFV) intrinsic to the atmosphere and short-climatic timescales that also involv...

Common approaches to diagnose systematic errors involve the computation of metrics aimed at providing an overall summary of the performance of the model in reproducing the particular variables of interest in the study, normally tied to specific spatial and temporal scales. However, the evaluation of model performance is not always tied to the under...

Python interface for IRI’s Climate Predictability Tool (CPT), a widely used research and application Model Output Statistics/Prediction toolbox. Publicly available: GitHub. Automatically downloads required observations (TRMM, CPC Unified) and S2S model data from the IRI Data Library (S2S Database and SubX –ECMWF, CFSv2, GEFS, others are being in...

The aim of this research is to evaluate the temperature outputs of climate forecasting systems over Iran. The analysis is provided based on Atmosphere-Ocean Coupled General Circulation Models from North America Multi Model Ensemble (NMME). The skill of NMME individual models are evaluated in different initializations, of lead times (0-month, 1-mont...

The International Research Institute for Climate and Society Data Li- brary (IRIDL) is a powerful and freely accessible online data repository and analysis web-service that allows a user to view, analyze, and down- load hundreds of terabytes of climate-related data through a standard web browser in a computer or a smartphone. A wide variety of oper...

Common approaches to diagnose systematic errors involve the computation of metrics aimed at providing an overall summary of the performance of the model in reproducing the particular variables of interest in the study, normally tied to specific spatial and temporal scales. However, the evaluation of model performance is not always tied to the under...

Recent research has highlighted the potential for improving predictive skill at the sub-seasonal timescale, which could be the basis for enhanced, actionable forecasts for climate services involving water and disaster management, health, energy and food security. The WMO's World Weather and World Climate Research Programmes Subseasonal-to-Seasonal...

The sub-seasonal to seasonal prediction project (S2S) is a 5-year project, established in 2013 by the World Weather Research Program (WWRP) and the World Climate Research Program (WCRP). This paper briefl y describes the S2S project in the context of extended range prediction of extreme events. We provide evidence that S2S forecasts have the potent...

The daily characteristics of tropical rainfall, such as rainfall frequency, are important applications-relevant targets for sub-seasonal to seasonal (S2S) forecasts. Their potential predictability is assessed here based on observational estimates of spatial coherence of tropical rainfall anomalies estimated from the mean spatial autocorrelation in...

The aim of this research is to evaluate a statistical method for downscaling the precipitation output of a number of Coupled General Circulation Models issuing seasonal forecasts 9 month in advance. Canonical Correlation Analysis (CCA) is applied for post-processing precipitation from the North American Multi-model Ensemble (NMME) project. The anal...

An assessment is made of the ability of general circulation models in the CMIP5 ensemble to reproduce observed modes of low-frequency winter/spring precipitation variability in the region of the Upper Indus basin (UIB) in south-central Asia. This season accounts for about two thirds of annual precipitation totals in the UIB and is characterized by...

Abstract This paper provides a summary of the Workshop on Sub-Seasonal to Seasonal (S2S) Predictability of Extreme Weather and Climate, held at Columbia University, December 6–7, 2016. The 2-day workshop was attended by over 100 people and took stock of recent developments in Sub-seasonal to Seasonal predictability, S2S extreme weather phenomena, a...

The spatial coherence of interannual variations of sub-seasonal to seasonal anomalies in Indian summer monsoon rainfall is investigated at 0.25° spatial resolution using various metrics, including estimates of the number of degrees of freedom, the spatial scale of daily wet " patches ", as well as relationships between local and regional-scale rain...

Subseasonal forecast skill over the broadly defined North American (NAM), West African (WAM) and Asian (AM) summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS) at sub-monthly lead times. Extended Logistic Regression (ELR) is used to produce probabilistic forecasts of weekly and week 3–4 averages of precipitation wit...

In this study we discuss probabilistic forecasts of Citarum River streamflow, which supplies 80 % of the water demands in Jakarta, Indonesia, based on general circulation model (GCM) output, for the September–November (SON) season. Retrospective forecasts of precipitation made over the period 1982–2010 with two coupled-ocean atmosphere GCMs, initia...

Streamflow forecasts at multiple time scales provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such forecasts in lieu of traditional operation and allo...

Discrete-time hidden Markov models are a broadly useful class of latent-variable models with applications in areas such as speech recognition, bioinformatics, and climate data analysis. It is common in practice to introduce temporal non-homogeneity into such models by making the transition probabilities dependent on time-varying exogenous input var...

Discrete-time hidden Markov models are a broadly useful class of latent-variable models with applications in areas such as speech recognition, bioinformatics, and climate data analysis. It is common in practice to introduce temporal non-homogeneity into such models by making the transition probabilities dependent on time-varying exogenous input var...

Any potential predictability will be of great value to cope with climate extremes, variability and change. Although hydro-energy, agricultural and water resource system planners require this information at seasonal to inter-annual time ranges, they have proven reluctant to incorporate climate forecasts into the decision making process. One reason l...

We propose a seamless diagnostic framework for coupled circulation models that can be used to catalogue their similarity to observations through timescales, and suggest model improvements.

Daily atmospheric circulation regimes can often be classified in robust clusters, or weather types (WTs), that describe available synoptic states of the region under study. Figuratively, these patterns can be seen to represent letters of an ‘alphabet’ that could be used to study the occurrence of impactful phenomena. For example, it has been shown...

This paper addresses the effect of interannual variability in jetstream orientation on weather systems over the North Atlantic basin (NAB). The observational analysis relies on 65 years of NCEP-NCAR reanalysis (1948–2012). The total daily kinetic energy of the geostrophic wind (GTKE) is taken as a measure of storm activity over the North Atlantic....

Potential and real predictive skill of the frequency of extreme rainfall in South East South America for the December-February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one timescale contribute to the predictability at another scale, i.e., taking into account the interference of differ...

The April 2011 floods in the Ohio River Basin and in the lower Mississippi River region were the latest of a set of major such flooding events recorded over the 20th century (defined in terms of a 10 year return maximum in stream flow). The questions of whether the recent 2011 event herald a return of more frequent flooding, and the degree of poten...

The physical mechanisms and predictability associated with extreme daily rainfall in South East South America (SESA) are investigated for the December-February season in a two-part study. Through a k-mean analysis, this first paper identifies a robust set of daily circulation regimes that are used to link the frequency of rainfall extreme events wi...

A Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional characteristics of precipitation at each site over time, while the Markovian transitions between discrete states represent seasonal...

Daily rainfall occurrence and amount at 55 sta- tions over New Caledonia (NC, 20°S, 166°E) are exam- ined throughout the calendar year during 1980–2010 using a Hidden Markov Model (HMM). Daily rainfall variability is described in terms of six discrete rainfall states iden- tified by the HMM. Three states are interpreted as trade wind regimes associ...

Daily rainfall occurrence and amount at 55 stations over New Caledonia (NC, 20◦S, 166◦ E) are examined throughout the calendar year during 1980–2010 using a Hidden Markov Model (HMM). Daily rainfall variability is described in terms of six discrete rainfall states identified by the HMM. Three states are interpreted as trade wind regimes associated...

The prediction skill of precipitation at submonthly time scales during the boreal summer season is investigated based on hindcasts from three global ensemble prediction systems (EPSs). The results, analyzed for lead times up to 4 weeks, indicate encouraging correlation skill over some regions, particularly over the Maritime Continent and the equato...

A new World Weather Research Program/World Climate Research Program (WWRP/WCRP initiative on subseasonal to seasonal (S2S) prediction has recently been launched to foster collaboration and research in the weather and climate communities, with the goals of improving forecast skill and physical understanding, promoting forecast uptake by operational...

Six weather types (WTs) are computed across the Maritime Continent during austral summer (September–April) using cluster analysis of unfiltered, daily, low-level winds at 850 hPa, by a k-means algorithm. This approach is shown to provide a unified view of the interactions across scales, from island-scale diurnal circulations to large-scale interann...

We present a Bayesian scheme for the downscaling of daily rainfall over a network of stations. Rainfall is modeled locally as a state-dependent mixture, with the states progressing in time as a first-order Markov process. The Markovian transition matrix, as well as the local state distributions, are dependent on exogenous covariates via generalized...

Outline of the talk: 1 Multivariate Singular Spectrum Analysis 2 Varimax rotation 3 Cluster synchronization 4 Significance test 5 Interannual variability in the North Atlantic Ocean

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to...

In this paper, precipitation outputs from retrospective seasonal forecasts made by nine General Circulation Models (GCMs) are used to investigate historical Indian summer monsoon seasonal rainfall variability and predictability over India. The observed data is taken from the India Meteorological Department whereas GCMs are obtained from the Interna...

Advances in our fundamental understanding of the physical climate system provided the necessary scientific underpinnings for the routine production of reliable seasonal climate forecasts and ultimately, the birth of the International Research Institute for Climate and Society (IRI). While recognizing that the successful adoption of climate informat...

A multiscale-modeling framework for daily rainfall is considered and diagnostic results are presented for an application to the winter season in Northwest India. The daily rainfall process is considered to follow a Hidden Markov Model (HMM), with the hidden states assumed to be an unknown random function of slowly varying climatic modulation of the...

Variability of Indian summer monsoon local-scale onset dates is investigated at 1-degree spatial resolution by applying an agronomic definition (i.e. the first significant rains without a potentially crop-threatening dry spell thereafter) to gridded observed daily rainfall data (1901–2004). Median onset dates compare well with previous estimates. T...

A climate-informed and climate-ready world is possible. Large investments are being made toward adaptation and resilience to climate change, but many of those investments are separated from the more immediate climate-related vulnerabilities and opportunities that society faces. Information is increasingly available that could be used to guide actio...

It is important to investigate potential changes in temperature, precipitation and solar radiation for assessing the impacts of future climate change on agricultural production for specific regions. In this study, climate scenarios of precipitation, temperature and solar radiation for the North China Plain (NCP) were constructed in terms of stochas...

This paper presents a predictability study of the Madden-Julian Oscillation (MJO) that relies on combining empirical model reduction (EMR) with the "past-noise fore-casting" (PNF) method. EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity, seasonality and serial correlation in the estim...

In this study, canonical correlation analysis (CCA) has been used to statistically downscale the seasonal predictions of the Indian summer monsoon rainfall (ISMR) from a global spectral model. An extensive diagnostic study of the global model products and observed data for the period 1981–2008 indicates that while the predictions of rainfall anomal...

Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteor...