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The World Climate Research Programme (WCRP) envisions a world "that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind." This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization. [ABSTRACT FROM AUTHOR]

In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four‐month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well‐tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large‐scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden‐Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations. Plain Language Summary: We present the world's first global simulation of an entire season of the Earth's atmosphere with 1.4 km average grid spacing and the top of the modeled atmosphere as high as 80 km. Albeit only a single realization due to its considerable computational cost, the resulting model output provides a reference and guidance for future simulations. For illustration we compare to simulations at 9 km grid spacing that represent the state of the art in numerical weather prediction and are still considerably finer when compared to models that are used for climate projections today. Thanks to its unprecedented detail, the simulation output will support future model development and satellite mission planning and may be seen as a prototype contribution to a future digital twin of our Earth. Key Points: A unique simulation with 1.4 km average grid spacing is presented for model development and process evaluationThe 1.4 km simulation shows remarkable fidelity with respect to the well‐calibrated simulation at 9 km with parametrized deep convectionSwitching off deep convection at a too coarse resolution (9 km) generates too strong convective gravity waves [ABSTRACT FROM AUTHOR]

ABSTRACT While seasonal outlooks have been operational for many years, until recently the extended-range timescale referred to as subseasonal-to-seasonal ( S2S) has received little attention. S2S prediction fills the gap between short-range weather prediction and long-range seasonal outlooks. Decisions in a range of sectors are made in this extended-range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications-relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision-making and valuation of forecasts, to enhance the benefits of 'climate services' approaches for extended-range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application-ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon. [ABSTRACT FROM AUTHOR]

This study examines El Niño-Southern Oscillation (ENSO) prediction in Project Minerva, a recent collaboration between the Center for Ocean-Land-Atmosphere Studies (COLA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The focus is primarily on the impact of the atmospheric horizontal resolution on ENSO prediction, but the effect from different ensemble sizes is also discussed. Particularly, three sets of 7-month hindcasts performed with ECMWF prediction system are compared, starting from 1 May (1 November) during 1982-2011 (1982-2010): spectral T319 atmospheric resolution with 15 ensembles, spectral T639 with 15 ensembles, and spectral T319 with 51 ensembles. The analysis herein shows that simply increasing either ensemble size from 15 to 51 or atmospheric horizontal resolution from T319 to T639 does not necessarily lead to major improvement in the ENSO prediction skill with current climate models. For deterministic prediction skill metrics, the three sets of predictions do not produce a significant difference in either anomaly correlation or root-mean-square error (RMSE). For probabilistic metrics, the increased atmospheric horizontal resolution generates larger ensemble spread, and thus increases the ratio between the intraensemble spread and RMSE. However, there is little change in the categorical distributions of predicted SST anomalies, and consequently there is little difference among the three sets of hindcasts in terms of probabilistic metrics or prediction reliability. [ABSTRACT FROM AUTHOR]

The article reports on the Experimental Madden-Julian Oscillation (MJO) Prediction Project (EMPP) during 2006. The project provides real-time weather and climate information and predictions for different type of applications. MJO represents a repeatable, low-frequency phenomenon. The article describes the history, objectives, status, capabilities, and plans of EMPP. Samples of forecast products are presented and explained. Verifications of tropical velocity potential for different weather forecasts models are also discussed.

The intraseasonal oscillation (e.g., the MJO and the BSISO) is the major source of predictability of extreme weather in a S2S time scale (e.g., 3-4 weeks). Many authors examined the predictability of the MJO and the BSISO in subjectively selected months. Recently, Kikuchi et al. (2016) applied a method which can objectively determine which ISO mode is dominant in a given day to a cloud-system resolving climate simulation data. They pointed out that the model has the BSISO bias even in boreal winter and simulated amplitude of ISO is about half of the observation. Considering that the models drift toward their own climatology in some timescales, it is helpful for model improvement to know how the drift occurs. Here we applied Kikuchi's method to the S2S data. All the S2S models examined show the BSISO bias even in boreal winter and some of those monotonically increase in the BSISO appearance frequency with the lead time. This result obviously indicates that the BSISO bias in boreal winter occurs from the drift of the model climatology. We will discuss what causes the drift at the presentation. [ABSTRACT FROM AUTHOR]

Sudden stratospheric warmings (SSWs) are significant source of enhanced subseasonal predictability, but whether this source is untapped in operational models remains an open question. Here we report on the prediction of the SSW on 12 February 2018, its dynamical precursors, and surface climate impacts by an ensemble of dynamical forecast models. The ensemble forecast from 1 February predicted 3 times increased odds of an SSW compared to climatology, although the lead time for SSW prediction varied among individual models. Errors in the forecast location of a Ural high and underestimated magnitude of upward wave activity flux reduced SSW forecast skill. Although the SSW's downward influence was not well forecasted, the observed northern Eurasia cold anomaly following SSW was predicted, albeit with a weaker magnitude, due to persistent tropospheric anomalies. The ensemble forecast from 8 February predicted the SSW, its subsequent downward influence, and a long‐lasting cold anomaly at the surface. Plain Language Summary: Predicting climate anomalies more than 2 weeks ahead remains a challenging task. However, there are periods when climate is more predictable. In particular, it is known that in the wintertime Northern Hemisphere midlatitudes, climate may be more predictable during several weeks following sudden stratospheric warmings (SSWs), when a cold stratospheric polar vortex suddenly breaks and the stratosphere warms. In this article we evaluate quality of monthly forecasts by several up‐to‐date forecast systems during a sudden stratospheric warming observed in February 2018 focusing both on predictability of the stratospheric anomalies and on predictability of their surface impacts. We find that models predicted enhanced probability of sudden stratospheric warming at least 11 days before the observed event. Four days before the event it was forecast by all models with a very high certainty. We further show that successful prediction of the SSW required correct forecasting of anticyclonic anomaly over Ural and associated upward propagation of planetary‐scale atmospheric waves. The observed cold anomaly across Eurasia in February 2018 was predicted more than 2 weeks ahead although at this lead time the predictability was more likely associated with persistence of tropospheric anomalies in the forecast, with smaller downward influence of the stratospheric anomalies. Key Points: Subseasonal multimodel ensemble predicted increased odds of a large sudden stratospheric warming in February 2018 at a lead time of 11 daysExtended cold anomaly over Eurasia in February 2018 was predicted more than 2 weeks ahead, but its intensity varied among individual modelsAccurate forecasting of a Ural high and associated upward wave activity flux are essential for improved prediction of stratospheric warming [ABSTRACT FROM AUTHOR]