The impact of air–sea interactions on the representation of tropical precipitation extremes

The impacts of air–sea interactions on the representation of tropical precipitation extremes are investigated using an atmosphere–ocean-mixed-layer coupled model. The coupled model is compared to two atmosphere-only simulations driven by the coupled model sea-surface temperatures (SSTs): one with 31-day running means (31d), the other with a repeating mean annual cycle. This allows separation of the effects of inter-annual SST variability from those of coupled feedbacks on shorter timescales. Crucially, all simulations have a consistent mean state with very small SST biases against present-day climatology. 31d overestimates the frequency, intensity and persistence of extreme tropical precipitation relative to the coupled model, likely due to excessive SST-forced precipitation variability. This implies that atmosphere-only attribution and time-slice experiments may overestimate the strength and duration of precipitation extremes. In the coupled model, air–sea feedbacks damp extreme precipitation, through negative local thermodynamic feedbacks between convection, surface fluxes and SST

Prediction errors of tropical cyclones in the western north Pacific in the Met Office global forecast model

The prediction of Tropical Cyclones (TCs) in the Western North Pacific (WNP) and the Philippines Area of Responsibility (PAR) has been explored in the UK Met Office (UKMO) global forecasting system over a 10 year period at 0-7 day lead times. Both the high resolution deterministic and lower resolution ensemble systems have been considered. Location errors for verification against the observations are comparable for the deterministic, control and ensemble mean forecasts, however, the ensemble spread indicates the ensemble is under-dispersive. Intensity error metrics, for pressure and surface winds, show large biases relative to the observations, with the smallest biases for the deterministic system. For the intensity metrics the ensemble spread shows the ensemble is severely under-dispersive primarily due to the large errors relative to the observations. Verification against the analyses show similar results to verification against the observations for location. This is also the case for the intensities albeit with smaller errors and less under-dispersion. The PAR region has larger intensity errors and biases and larger intensity ensemble spread compared with the broader WNP region. Forecast errors for location and intensity have reduced significantly with system upgrades over the period studied (2008-2017) for the deterministic and ensemble systems. Intensity errors for the latest configuration of the deterministic system at day 4 are smaller than the initial errors of all the earlier configurations for both pressure and winds. The Madden-–Julian oscillation (MJO) and Boreal Summer Intra-Seasonal Oscillation (BSISO) significantly affect the intensity forecast errors, but not the location errors. Intensity errors are lower at the initiation and for early lead times of the forecasts started in phases 6--7 and 7--8, when the MJO and BSISO are active in the WNP. These reduced errors appear to result mainly from the variations in intensity of the observed storms with MJO and BSISO phases, though the initial states of the forecasts are also affected. Over the studied period, the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic and ensemble systems have lower errors and biases for both location and intensity than the UKMO forecast systems

Connection between sea surface anomalies and atmospheric quasi-stationary waves

Large scale, quasi-stationary atmospheric waves (QSWs) are known to be strongly connected with extreme events and general weather conditions. Yet, despite their importance, there is still a lack of understanding about what drives variability in QSW. This study is a step towards this goal, and identifies three statistically significant connections between QSWs and sea surface anomalies (temperature and ice cover) by applying a maximum covariance analysis technique to reanalysis data (1979-2015). The two most dominant connections are linked to the El Ni\~no Southern Oscillation and the North Atlantic Oscillation. They confirm the expected relationship between QSWs and anomalous surface conditions in the tropical Pacific and the North Atlantic, but they cannot be used to infer a driving mechanism or predictability from the sea surface temperature or the sea ice cover to the QSW. The third connection, in contrast, occurs between late winter to early spring Atlantic sea ice concentrations and anomalous QSW patterns in the following late summer to early autumn. This new finding offers a pathway for possible long term predictability of late summer QSW occurrence

Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent

A multi-season convection-permitting regional climate simulation of the Maritime Continent (MC) using the Met Office Unified Model (MetUM) with 2.2 km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere–ocean coupling with the multi-column K profile parametrisation (KPP) mixed-layer ocean model in atmospheric convection-permitting climate simulations. Comparisons are made against a convection-parametrised simulation in which it is nested and which in turn derives boundary conditions from the ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability in the two simulations compared against observational datasets. The models have both minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves, and relationship between SST and precipitation are all improved in the convection-permitting model compared to the convection-parametrised model. The Madden–Julian oscillation (MJO) is present in both models with a faster-than-observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries

Coupled climate response to Atlantic Multidecadal Variability in a multi-model multi-resolution ensemble

North Atlantic sea surface temperatures (SSTs) underwent pronounced multidecadal variability during the twentieth and early twenty-first century. We examine the impacts of this Atlantic Multidecadal Variability (AMV), also referred to as the Atlantic Multidecadal Oscillation (AMO), on climate in an ensemble of five coupled climate models at both low and high spatial resolution. We use a SST nudging scheme specified by the Coupled Model Intercomparision Project’s Decadal Climate Prediction Project Component C (CMIP6 DCPP-C) to impose a persistent positive/negative phase of the AMV in the North Atlantic in coupled model simulations; SSTs are free to evolve outside this region. The large-scale seasonal mean response to the positive AMV involves widespread warming over Eurasia and the Americas, with a pattern of cooling over the Pacific Ocean similar to the Pacific Decadal Oscillation (PDO), together with a northward displacement of the inter-tropical convergence zone (ITCZ). The accompanying changes in global atmospheric circulation lead to widespread changes in precipitation. We use Analysis of Variance (ANOVA) to demonstrate that this large-scale climate response is accompanied by significant differences between models in how they respond to the common AMV forcing, particularly in the tropics. These differences may arise from variations in North Atlantic air-sea heat fluxes between models despite a common North Atlantic SST forcing pattern. We cannot detect a widespread effect of increased model horizontal resolution in this climate response, with the exception of the ITCZ, which shifts further northwards in the positive phase of the AMV in the higher resolution configuratio

Half a degree additional warming, prognosis and projected impacts (HAPPI): Background and experimental design

Abstract. The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios.Large ensembles of simulations (&gt;  50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006–2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario. </jats:p

A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part II: intraseasonal variability

The causes of subseasonal precipitation variability in China are investigated using observations and reanalysis data for extended winter (November–April) and summer (May–October) seasons from 1982 to 2007. For each season, the three dominant regions of coherent intraseasonal variability are identified with Empirical Orthogonal Teleconnection (EOT) analysis. While previous studies have focused on particular causes for precipitation variability or on specific regions, here a comprehensive analysis is carried out with an objective method. Furthermore, the associated rainfall anomaly timeseries are tied to specific locations in China, which facilitates their interpretation. To understand the underlying processes associated with spatially coherent patterns of rainfall variability, fields from observations and reanalysis are regressed onto EOT timeseries. The three dominant patterns in winter together explain 43% of the total space–time variance and have their origins in midlatitude disturbances that appear two pentads in advance. Winter precipitation variability along the Yangtze River is associated with wave trains originating over the Atlantic and northern Europe, while precipitation variability in southeast China is connected to the Mediterranean storm track. In summer, all patterns have a strong relationship with the Boreal Summer Intraseasonal Oscillation and are modulated by the seasonal cycle of the East Asian summer monsoon. The wet and dry phases of the regional patterns can substantially modulate the frequency of daily rainfall across China. The discovered links between weather patterns, precursors, and effects on local and remote precipitation may provide a valuable basis for hydrological risk assessments and the evaluation of numerical weather prediction models

The contribution of tropical cyclones to the atmospheric branch of Middle America's hydrological cycle using observed and reanalysis tracks

Middle America is affected by tropical cyclones (TCs) from the Eastern Pacific and the North Atlantic Oceans. We characterize the regional climatology (1998-2016) of the TC contributions to the atmospheric branch of the hydrological cycle, from May to December. TC contributions to rainfall are quantified using Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product 3B42 and TC tracks derived from three sources: the International Best Track Archive for Climate Stewardship (IBTrACS), and an objective feature tracking method applied to the Japanese 55-year and ERA-Interim reanalyses. From July to October, TCs contribute 10-30% of rainfall over the west and east coast of Mexico and central Mexico, with the largest monthly contribution during September over the Baja California Peninsula (up to 90%). TCs are associated with 40-60% of daily extreme rainfall (above the 95th percentile) over the coasts of Mexico. IBTrACS and reanalyses agree on TC contributions over the Atlantic Ocean but disagree over the Eastern Pacific Ocean and continent; differences over the continent are mainly attributed to discrepancies in TC tracks in proximity to the coast and TC lifetime. Reanalysis estimates of TC moisture transports show that TCs are an important moisture source for the regional water budget. TC vertically integrated moisture flux (VIMF) convergence can turn regions of weak VIMF divergence by the mean circulation into regions of weak VIMF convergence. We discuss deficiencies in the observed and reanalysis TC tracks, which limit our ability to quantify robustly the contribution of TCs to the regional hydrological cycle

Evaluation of climate simulations produced with the Brazilian Global Atmospheric Model version 1.2

This paper presents an evaluation of climate simulations produced by the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) of the Center for Weather Forecast and Climate Studies (CPTEC). The model was run over the 1975-2017 period at two spatial resolutions, corresponding to ~180 and ~100 km, both with 42 vertical levels, following most of the Atmospheric Model Intercomparison Project (AMIP) protocol. In this protocol, observed sea surface temperatures (SSTs) are used as boundary conditions for the atmospheric model. Four ensemble members were run for each of the two resolutions. A series of diagnostics was computed for assessing the model's ability to represent the top of the atmosphere (TOA) radiation, atmospheric temperature, circulation and precipitation climatological features. The representation of precipitation interannual variability, El Niño-Southern Oscillation (ENSO) precipitation teleconnections, the Madden and Julian Oscillation (MJO) and daily precipitation characteristics was also assessed. The model at both resolutions reproduced many observed temperature, atmospheric circulation and precipitation climatological features, despite several identified biases. The model atmosphere was found to be more transparent than the observations, leading to misrepresentation of cloud-radiation interactions. The net cloud radiative forcing, which produces a cooling effect on the global mean climate at the TOA, was well represented by the model. This was found to be due to the compensation between both weaker longwave cloud radiative forcing (LWCRF) and shortwave cloud radiative forcing (SWCRF) in the model compared to the observations. The model capability to represent inter-annual precipitation variability at both resolutions was found to be linked to the adequate representation of ENSO teleconnections. However, the model produced weaker than observed convective activity associated with the MJO. Light daily precipitation over the southeast of South America and other climatologically similar regions was diagnosed to be overestimated, and heavy daily precipitation underestimated by the model. Increasing spatial resolution helped to slightly reduce some of the diagnosed biases. The performed evaluation identified model aspects that need to be improved. These include the representation of polar continental surface and sea ice albedo, stratospheric ozone, low marine clouds, and daily precipitation features, which were found to be larger and last longer than the observed features

A perspective for advancing climate prediction services in Brazil

The Climate Science for Service Partnership Brazil (CSSP-Brazil) project provides Brazil and UK partners the opportunity to address important challenges faced by the climate modeling community, including the need to develop subseasonal and seasonal prediction and climate projection services. This paper provides an overview of the climate modeling and prediction research conducted through CSSP-Brazil within the context of a framework to advance climate prediction services in Brazil that includes a research-to-services (R2S) and a services-to-research (S2R) feedback pathway. The paper also highlights plans to advance scientific understanding and capability to produce beneficial climate knowledge and new products to improve climate prediction services to support decisions in various industries in Brazil. Policy-relevant outcomes from climate modeling and prediction exercises illustrated in this paper include supporting stakeholders with climate information provided from weeks to months ahead for (a) improving water management strategies for human consumption, navigation, and agricultural and electricity production; (b) defining crop variety and calendars for food production; and (c) diversifying energy production with alternatives to hydropower