An objective tropical Atlantic sea surface temperature gradient index for studies of south Amazon dry-season climate variability and change

Future changes in meridional sea surface temperature (SST) gradients in the tropical Atlantic could influence Amazon dry-season precipitation by shifting the patterns of moisture convergence and vertical motion. Unlike for the El Niño-Southern Oscillation, there are no standard indices for quantifying this gradient. Here we describe a method for identifying the SST gradient that is most closely associated with June–August precipitation over the south Amazon. We use an ensemble of atmospheric general circulation model (AGCM) integrations forced by observed SST from 1949 to 2005. A large number of tropical Atlantic SST gradient indices are generated randomly and temporal correlations are examined between these indices and June–August precipitation averaged over the Amazon Basin south of the equator. The indices correlating most strongly with June–August southern Amazon precipitation form a cluster of near-meridional orientation centred near the equator. The location of the southern component of the gradient is particularly well defined in a region off the Brazilian tropical coast, consistent with known physical mechanisms. The chosen index appears to capture much of the Atlantic SST influence on simulated southern Amazon dry-season precipitation, and is significantly correlated with observed southern Amazon precipitation

ENSO in a changing climate

The El Niño–Southern Oscillation (ENSO) phenomenon is a naturally occurring climate fluctuation, which originates in the tropical Pacific region and affects ecosystems, agriculture, freshwater supplies, hurricanes and other severe weather events worldwide (Goddard and Dilley 2005; McPhaden et al. 2006). Despite considerable progress in our understanding of the impact of climate change on many of the processes that contribute to ENSO variability (e.g., Collins et al. 2010), it is not yet possible to say whether ENSO activity will be enhanced or damped, or if the frequency or character of events will change in the coming decades (Vecchi and Wittenberg 2010). As changes in ENSO have the potential to be one of the largest manifestations of anthropogenic climate change, this status has profound impacts on the reliability of regional attribution of climate variability and change. Two main reasons can be invoked for these shortcomings. First there is a lack of long and comprehensive enough observations of the various ENSO processes to be able to detect past changes. It may be that we need to observe ENSO for another several decades to detect and attribute significant ENSO changes (Wittenberg 2009; Stevenson et al. 2012). Second, as ENSO involves a complex interplay of numerous ocean and atmospheric processes, accurately modeling this climate phenomenon with CGCMs, an

Northward propagation of the Inter Tropical Convergence Zone and strengthening of Indian summer monsoon rainfall

Since 2002, there has been a clear increase in Indian summer monsoon rainfall (ISMR). We demonstrate that this increase is associated with a change in the dynamics of the Intertropical Convergence Zone (ITCZ). Using a recently‐released reanalysis product from 1980‐2016, we show that the ITCZ has strengthened and propagated northward since 2002. Analysis of the total energy‐budget reveals an increase in energy divergence and atmospheric diabatic heating, which is consistent with the changes in the ITCZ. Although global aerosol optical depth shows a significant positive trend during 1980‐2016, it has declined over many parts of India since 2002. We put forward the hypothesis that this is the driver of the changing characteristics of the ITCZ. Our results suggest that changes in the dynamics of the ITCZ, together with changes in the energy/moisture‐budget, are responsible for the strengthening of ISMR since 2002, consistent with the emergence of a greenhouse‐gas induced signal

Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season

The Indian monsoon is an important component of Earth's climate system, accurate forecasting of its mean rainfall being essential for regional food and water security. Accurate measurement of the rainfall is essential for various water-related applications, the evaluation of numerical models and detection and attribution of trends, but a variety of different gridded rainfall datasets are available for these purposes. In this study, six gridded rainfall datasets are compared against the India Meteorological Department (IMD) gridded rainfall dataset, chosen as the most representative of the observed system due to its high gauge density. The datasets comprise those based solely on rain gauge observations and those merging rain gauge data with satellite-derived products. Various skill scores and subjective comparisons are carried out for the Indian region during the south-west monsoon season (June to September). Relative biases and skill metrics are documented at all-India and sub-regional scales. In the gauge-based (land-only) category, Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Global Precipitation Climatology Center (GPCC) datasets perform better relative to the others in terms of a variety of skill metrics. In the merged category, the Global Precipitation Climatology Project (GPCP) dataset is shown to perform better than the Climate Prediction Center Merged Analysis of Precipitation (CMAP) for the Indian monsoon in terms of various metrics, when compared with the IMD gridded data. Most of the datasets have difficulty in representing rainfall over orographic regions including the Western Ghats mountains, in north-east India and the Himalayan foothills. The wide range of skill scores seen among the datasets and even the change of sign of bias found in some years are causes of concern. This uncertainty between datasets is largest in north-east India. These results will help those studying the Indian monsoon region to select an appropriate dataset depending on their application and focus of research

High sensitivity of future global warming to land carbon cycle processes

Unknowns in future global warming are usually assumed to arise from uncertainties either in the amount of anthropogenic greenhouse gas emissions or in the sensitivity of the climate to changes in greenhouse gas concentrations. Characterizing the additional uncertainty in relating CO2 emissions to atmospheric concentrations has relied on either a small number of complex models with diversity in process representations, or simple models. To date, these models indicate that the relevant carbon cycle uncertainties are smaller than the uncertainties in physical climate feedbacks and emissions. Here, for a single emissions scenario, we use a full coupled climate–carbon cycle model and a systematic method to explore uncertainties in the land carbon cycle feedback. We find a plausible range of climate–carbon cycle feedbacks significantly larger than previously estimated. Indeed the range of CO2 concentrations arising from our single emissions scenario is greater than that previously estimated across the full range of IPCC SRES emissions scenarios with carbon cycle uncertainties ignored. The sensitivity of photosynthetic metabolism to temperature emerges as the most important uncertainty. This highlights an aspect of current land carbon modelling where there are open questions about the potential role of plant acclimation to increasing temperatures. There is an urgent need for better understanding of plant photosynthetic responses to high temperature, as these responses are shown here to be key contributors to the magnitude of future change

Effect of AMOC collapse on ENSO in a high resolution general circulation model

We look at changes in the El Niño Southern Oscillation (ENSO) in a high-resolution eddy-permitting climate model experiment in which the Atlantic Meridional Circulation (AMOC) is switched off using freshwater hosing. The ENSO mode is shifted eastward and its period becomes longer and more regular when the AMOC is off. The eastward shift can be attributed to an anomalous eastern Ekman transport in the mean equatorial Pacific ocean state. Convergence of this transport deepens the thermocline in the eastern tropical Pacific and increases the temperature anomaly relaxation time, causing increased ENSO period. The anomalous Ekman transport is caused by a surface northerly wind anomaly in response to the meridional sea surface temperature dipole that results from switching the AMOC off. In contrast to a previous study with an earlier version of the model, which showed an increase in ENSO amplitude in an AMOC off experiment, here the amplitude remains the same as in the AMOC on control state. We attribute this difference to variations in the response of decreased stochastic forcing in the different models, which competes with the reduced damping of temperature anomalies. In the new high-resolution model, these effects approximately cancel resulting in no change in amplitude

The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1

Abstract. Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation

A low-eccentricity migration pathway for a 13-h-period Earth analogue in a four-planet system

It is commonly accepted that exoplanets with orbital periods shorter than one day, also known as ultra-short-period (USP) planets, formed further out within their natal protoplanetary disks before migrating to their current-day orbits via dynamical interactions. One of the most accepted theories suggests a violent scenario involving high-eccentricity migration followed by tidal circularization. Here we present the discovery of a four-planet system orbiting the bright (V = 10.5) K6 dwarf star TOI-500. The innermost planet is a transiting, Earth-sized USP planet with an orbital period of ~13 hours, a mass of 1.42 \ub1 0.18 M⊕, a radius of 1.166−0.058+0.061R⊕ and a mean density of 4.89−0.88+1.03gcm−3. Via Doppler spectroscopy, we discovered that the system hosts 3 outer planets on nearly circular orbits with periods of 6.6, 26.2 and 61.3 days and minimum masses of 5.03 \ub1 0.41 M⊕, 33.12 \ub1 0.88 M⊕ and 15.05−1.11+1.12M⊕, respectively. The presence of both a USP planet and a low-mass object on a 6.6-day orbit indicates that the architecture of this system can be explained via a scenario in which the planets started on low-eccentricity orbits then moved inwards through a quasi-static secular migration. Our numerical simulations show that this migration channel can bring TOI-500 b to its current location in 2 Gyr, starting from an initial orbit of 0.02 au. TOI-500 is the first four-planet system known to host a USP Earth analogue whose current architecture can be explained via a non-violent migration scenario

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media