Attribution of 2022 early-spring heatwave in India and Pakistan to climate change: lessons in assessing vulnerability and preparedness in reducing impacts

In March 2022, large parts over the north Indian plains including the breadbasket region, and southern Pakistan began experiencing prolonged heat, which continued into May. The event was exacerbated due to prevailing dry conditions in the region, resulting in devastating consequences for public health and agriculture. Using event attribution methods, we analyse the role of human-induced climate change in altering the chances of such an event. To capture the extent of the impacts, we choose March-April average of daily maximum temperature over the most affected region in India and Pakistan as the variable. In observations, the 2022 event has a return period of ~1-in-100 years. For each of the climate models, we then calculate the change in probability and intensity of a 1-in-100 year event between the actual and counterfactual worlds for quantifying the role of climate change. We estimate that human-caused climate change made this heatwave about 1°C hotter and 30 times more likely in the current, 2022 climate, as compared to the 1.2 °C cooler, pre-industrial climate. Under a future global warming of 2°C above pre-industrial levels, heatwaves like this are expected to become even more common (2–20 times more likely) and hotter (by 0-1.5°C) compared to now. Stronger and frequent heat waves in the future will impact vulnerable groups as conditions in some regions exceed limits for human survivability. Therefore, mitigation is essential for avoiding loss of lives and livelihood. Heat Action Plans (HAPs) have proved effective to help reduce heat-related mortality in both countries

Human-induced global ocean warming on multidecadal timescales

Large-scale increases in upper-ocean temperatures are evident in observational records1. Several studies have used well-established detection and attribution methods to demonstrate that the observed basin-scale temperature changes are consistent with model responses to anthropogenic forcing and inconsistent with model-based estimates of natural variability2,3,4,5. These studies relied on a single observational data set and employed results from only one or two models. Recent identification of systematic instrumental biases6 in expendable bathythermograph data has led to improved estimates of ocean temperature variability and trends7,8,9 and provide motivation to revisit earlier detection and attribution studies. We examine the causes of ocean warming using these improved observational estimates, together with results from a large multimodel archive of externally forced and unforced simulations. The time evolution of upper ocean temperature changes in the newer observational estimates is similar to that of the multimodel average of simulations that include the effects of volcanic eruptions. Our detection and attribution analysis systematically examines the sensitivity of results to a variety of model and data-processing choices. When global mean changes are included, we consistently obtain a positive identification (at the 1% significance level) of an anthropogenic fingerprint in observed upper-ocean temperature changes, thereby substantially strengthening existing detection and attribution evidence

Understanding global sea levels: past, present and future

The coastal zone has changed profoundly during the 20th century and, as a result, society is becoming increasingly vulnerable to the impact of sea-level rise and variability. This demands improved understanding to facilitate appropriate planning to minimise potential losses. With this in mind, the World Climate Research Programme organised a workshop (held in June 2006) to document current understanding and to identify research and observations required to reduce current uncertainties associated with sea-level rise and variability. While sea levels have varied by over 120 m during glacial/interglacial cycles, there has been little net rise over the past several millennia until the 19th century and early 20th century, when geological and tide-gauge data indicate an increase in the rate of sea-level rise. Recent satellite-altimeter data and tide-gauge data have indicated that sea levels are now rising at over 3 mm year−1. The major contributions to 20th and 21st century sea-level rise are thought to be a result of ocean thermal expansion and the melting of glaciers and ice caps. Ice sheets are thought to have been a minor contributor to 20th century sea-level rise, but are potentially the largest contributor in the longer term. Sea levels are currently rising at the upper limit of the projections of the Third Assessment Report of the Intergovernmental Panel on Climate Change (TAR IPCC), and there is increasing concern of potentially large ice-sheet contributions during the 21st century and beyond, particularly if greenhouse gas emissions continue unabated. A suite of ongoing satellite and in situ observational activities need to be sustained and new activities supported. To the extent that we are able to sustain these observations, research programmes utilising the resulting data should be able to significantly improve our understanding and narrow projections of future sea-level rise and variabilit

Robust warming of the global upper ocean

A large (similar to 10(23) J) multi-decadal globally averaged warming signal in the upper 300 m of the world's oceans was reported roughly a decade ago(1) and is attributed to warming associated with anthropogenic greenhouse gases(2,3). The majority of the Earth's total energy uptake during recent decades has occurred in the upper ocean(3), but the underlying uncertainties in ocean warming are unclear, limiting our ability to assess closure of sea-level budgets(4-7), the global radiation imbalance(8) and climate models(5). For example, several teams have recently produced different multi-year estimates of the annually averaged global integral of upper-ocean heat content anomalies (hereafter OHCA curves) or, equivalently, the thermosteric sea-level rise(5,9-16). Patterns of inter-annual variability, in particular, differ among methods. Here we examine several sources of uncertainty that contribute to differences among OHCA curves from 1993 to 2008, focusing on the difficulties of correcting biases in expendable bathythermograph (XBT) data. XBT data constitute the majority of the in situ measurements of upper-ocean heat content from 1967 to 2002, and we find that the uncertainty due to choice of XBT bias correction dominates among-method variability in OHCA curves during our 1993-2008 study period. Accounting for multiple sources of uncertainty, a composite of several OHCA curves using different XBT bias corrections still yields a statistically significant linear warming trend for 1993-2008 of 0.64 W m(-2) (calculated for the Earth's entire surface area), with a 90-per-cent confidence interval of 0.53-0.75 W m(-2)

Effect of surface restoring on subsurface variability in a climate model during 1949-2005

Initializing the ocean for decadal predictability studies is a challenge, as it requires reconstructing the little observed subsurface trajectory of ocean variability. In this study we explore to what extent surface nudging using well-observed sea surface temperature (SST) can reconstruct the deeper ocean variations for the 1949–2005 period. An ensemble made with a nudged version of the IPSLCM5A model and compared to ocean reanalyses and reconstructed datasets. The SST is restored to observations using a physically-based relaxation coefficient, in contrast to earlier studies, which use a much larger value. The assessment is restricted to the regions where the ocean reanalyses agree, i.e. in the upper 500 m of the ocean, although this can be latitude and basin dependent. Significant reconstruction of the subsurface is achieved in specific regions, namely region of subduction in the subtropical Atlantic, below the thermocline in the equatorial Pacific and, in some cases, in the North Atlantic deep convection regions. Beyond the mean correlations, ocean integrals are used to explore the time evolution of the correlation over 20-year windows. Classical fixed depth heat content diagnostics do not exhibit any significant reconstruction between the different existing observation-based references and can therefore not be used to assess global average time-varying correlations in the nudged simulations. Using the physically based average temperature above an isotherm (14 °C) alleviates this issue in the tropics and subtropics and shows significant reconstruction of these quantities in the nudged simulations for several decades. This skill is attributed to the wind stress reconstruction in the tropics, as already demonstrated in a perfect model study using the same model. Thus, we also show here the robustness of this result in an historical and observational context

Climate change and the South Asian summer monsoon

The vagaries of South Asian summer monsoon rainfall on short and long timescales impact the lives of more than one billion people. Understanding how the monsoon will change in the face of global warming is a challenge for climate science, not least because our state-of-the-art general circulation models still have difficulty simulating the regional distribution of monsoon rainfall. However, we are beginning to understand more about processes driving the monsoon, its seasonal cycle and modes of variability. This gives us the hope that we can build better models and ultimately reduce the uncertainty in our projections of future monsoon rainfall

The role of the intra-daily SST variability in the Indian monsoon variability and monsoon-ENSO–IOD relationships in a global coupled model

The impact of diurnal SST coupling and vertical oceanic resolution on the simulation of the Indian Summer Monsoon (ISM) and its relationships with El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events are studied through the analysis of four integrations of a high resolution Coupled General Circulation Model (CGCM), but with different configurations. The only differences between the four integrations are the frequency of coupling between the ocean and atmosphere for the Sea Surface Temperature (SST) parameter (2 vs. 24 h coupling) and/or the vertical oceanic resolution (31 vs. 301 levels) in the CGCM. Although the summer mean tropical climate is reasonably well captured with all the configurations of the CGCM and is not significantly modified by changing the frequency of SST coupling from once to twelve per day, the ISM–ENSO teleconnections are rather poorly simulated in the two simulations in which SST is exchanged only once per day, independently of the vertical oceanic resolution used in the CGCM. Surprisingly, when 2 h SST coupling is implemented in the CGCM, the ISM–ENSO teleconnection is better simulated, particularly, the complex lead-lag relationships between the two phenomena, in which a weak ISM occurs during the developing phase of an El Niño event in the Pacific, are closely resembling the observed ones. Evidence is presented to show that these improvements are related to changes in the characteristics of the model’s El Niño which has a more realistic evolution in its developing and decaying phases, a stronger amplitude and a shift to lower frequencies when a 2-hourly SST coupling strategy is implemented without any significant changes in the basic state of the CGCM. As a consequence of these improvements in ENSO variability, the lead relationships between Indo-Pacific SSTs and ISM rainfall resemble the observed patterns more closely, the ISM–ENSO teleconnection is strengthened during boreal summer and ISM rainfall power spectrum is in better agreement with observations. On the other hand, the ISM–IOD teleconnection is sensitive to both SST coupling frequency and the vertical oceanic resolution, but increasing the vertical oceanic resolution is degrading the ISM–IOD teleconnection in the CGCM. These results highlight the need of a proper assessment of both temporal scale interactions and coupling strategies in order to improve current CGCMs. These results, which must be confirmed with other CGCMs, have also important implications for dynamical seasonal prediction systems or climate change projections of the monsoon