2021 ◽  
Author(s):  
Paolo Ruggieri ◽  
Marianna Benassi ◽  
Stefano Materia ◽  
Daniele Peano ◽  
Constantin Ardilouze ◽  
...  

<p>Seasonal climate predictions leverage on many predictable or persistent components of the Earth system that can modify the state of the atmosphere and of relant weather related variable such as temprature and precipitation. With a dominant role of the ocean, the land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between land surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere both locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been investigated and documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of Autumn Eurasian snow in recent dynamical seasonal forecasts is therefore unclear. In this study we assess the role of Eurasian snow cover in a set of 5 operational seasonal forecast system characterized by a large ensemble size and a high atmospheric and oceanic resolution. Results are compemented with a set of targeted idealised simulations with atmospheric general circulation models forced by different snow cover conditions. Forecast systems reproduce realistically regional changes of the surface energy balance associated with snow cover variability. Retrospective forecasts and idealised sensitivity experiments converge in identifying a coherent change of the circulation in the Northern Hemisphere. This is compatible with a lagged but fast feedback from the snow to the Arctic Oscillation trough a tropospheric pathway.</p>


2021 ◽  
Author(s):  
Jonghun Kam ◽  
Sungyoon Kim ◽  
Joshua Roundy

<p>This study used the North American Multi-Model Ensemble (NMME) system to understand the role of near surface temperature in the prediction skill for US climate extremes. In this study, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC) or 2-meter air temperature (T2m) over the contiguous United States (CONUS) during 1982–2012. The strength of the PREC-T2m coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. This study also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (-0.65), and 2012 (-0.60) when the PREC-T2m coupling is weaker or stronger than the 1982–2012 climatology (ACC:-0.34). This study found that most of the NMME models show stronger (negative) PREC-T2m coupling than the observed coupling, indicating that they fail to reproduce interannual variability of the observed PREC-T2m coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong PREC-T2m coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Lastly, we explored how the forecasting skill for SSTs over north Pacific and Atlantic Oceans affects the forecasting skill for T2m and PREC over the US. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.</p>


2016 ◽  
Vol 29 (11) ◽  
pp. 4099-4119 ◽  
Author(s):  
Shan Li ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Xiaosong Yang ◽  
Anthony Rosati ◽  
...  

Abstract Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that use empirically set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model that includes large-scale feedbacks for cumulus convection and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.


2016 ◽  
Vol 7 (4) ◽  
pp. 851-861 ◽  
Author(s):  
Rasmus E. Benestad ◽  
Retish Senan ◽  
Yvan Orsolini

Abstract. We show how factorial regression can be used to analyse numerical model experiments, testing the effect of different model settings. We analysed results from a coupled atmosphere–ocean model to explore how the different choices in the experimental set-up influence the seasonal predictions. These choices included a representation of the sea ice and the height of top of the atmosphere, and the results suggested that the simulated monthly mean air temperatures poleward of the mid-latitudes were highly sensitivity to the specification of the top of the atmosphere, interpreted as the presence or absence of a stratosphere. The seasonal forecasts for the mid-latitudes to high latitudes were also sensitive to whether the model set-up included a dynamic or non-dynamic sea-ice representation, although this effect was somewhat less important than the role of the stratosphere. The air temperature in the tropics was insensitive to these choices.


2022 ◽  
Author(s):  
Peter Hitchcock ◽  
Amy Butler ◽  
Andrew Charlton-Perez ◽  
Chaim Garfinkel ◽  
Tim Stockdale ◽  
...  

Abstract. Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.


2020 ◽  
Author(s):  
Rachel White ◽  
Chloé Prodhomme ◽  
Georgios Fragkoulidis ◽  
Stefano Materia ◽  
Constantin Ardilouze

<p>Heat waves can have a devastating impact on human society and ecosystems, and thus improved understanding and predictability of such events would provide huge benefits. It has been shown previously that many extreme temperature events are associated with quasi-stationary, or recurrent, Rossby waves (hereafter QSWs). We show that these QSWs are often associated with atmospheric waveguides, providing some dynamical understanding of why such weather patterns persist. In the context of this framework, we study the subseasonal-to-seasonal (S2S) predictability of heatwaves, QSWs, and atmospheric waveguides. Operational seasonal forecasts can reproduce the observed climatological statistics of QSWs, and the observed connection between QSWs and extreme temperatures over Europe, although with some biases. To better understand the underlying dynamics of the seasonal forecast models, we explore whether such models are capable of reproducing the observed connection between QSWs and atmospheric waveguides,  linked to persistent, and thus high impact, extreme heat events. We examine the S2S predictability of atmospheric waveguides and high amplitude QSW events, to better understand the potential S2S predictability of heatwaves.</p>


2017 ◽  
Vol 30 (21) ◽  
pp. 8657-8671 ◽  
Author(s):  
Patrick D. Broxton ◽  
Xubin Zeng ◽  
Nicholas Dawson

Across much of the Northern Hemisphere, Climate Forecast System forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April–June than forecasts made later (e.g., on 1 April); furthermore, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April–June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April–June than later forecasts. Although the later forecasts of temperature are more accurate, earlier forecasts of SWE are more realistic, suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biases when a more realistic amount of snow is on the ground. Furthermore, SWE differences in earlier versus later forecasts are found to much more strongly affect April–June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring–summer transition over snowy regions.


2016 ◽  
Author(s):  
Rasmus E. Benestad ◽  
Retish Senan ◽  
Yvan Orsolini

Abstract. We demonstrate how factorial regression can be used to analyse numerical model experiments, testing the effect of different model settings. We analysed results from a coupled atmosphere-ocean model to explore how the different choices in the experimental set-up influence the seasonal predictions. These choices included a representation of the sea-ice and the choice of top of the atmosphere, and the results suggested that the simulated monthly mean temperatures poleward of the mid-latitudes are highly sensitivity to the specification of the top of the atmosphere, interpreted as the presence or absence of a stratosphere. The seasonal forecasts for the mid-to-high latitudes were also sensitive to whether the model set-up included a dynamic or non-dynamics sea-ice representation, although this effect was less important than the role of the stratosphere. The temperature in the tropics was insensitive to these choices.


2008 ◽  
Vol 21 (15) ◽  
pp. 3617-3641 ◽  
Author(s):  
Andrew M. Carleton ◽  
David J. Travis ◽  
Jimmy O. Adegoke ◽  
David L. Arnold ◽  
Steve Curran

Abstract In Part I of this observational study inquiring into the relative influences of “top down” synoptic atmospheric conditions and “bottom up” land surface mesoscale conditions in deep convection for the humid lowlands of the Midwest U.S. Central Corn Belt (CCB), the composite atmospheric environments for afternoon and evening periods of convection (CV) versus no convection (NC) were determined for two recent summers (1999 and 2000) having contrasting precipitation patterns and amounts. A close spatial correspondence was noted between composite synoptic features representing baroclinity and upward vertical motion with the observed precipitation on CV days when the “background” (i.e., free atmosphere) wind speed exceeded approximately 10 m s−1 at 500 hPa (i.e., “stronger flow”). However, on CV days when wind speeds were <∼10 m s−1 (i.e., “weaker flow”), areas of increased precipitation can be associated with synoptic composites that are not so different from those for corresponding NC days. From these observations, the presence of a land surface mesoscale influence on deep convection and precipitation is inferred that is better expressed on weaker flow days. Climatically, a likely candidate for enhancing low-level moisture convergence to promote deep convection are the quasi-permanent vegetation boundaries (QPVBs) between the two major land use and land cover (LULC) types of crop and forest that characterize much of the CCB. Accordingly, in this paper the role of these boundaries on summer precipitation variations for the CCB is extracted in two complementary ways: 1) for contrasting flow day types in the summers 1999 and 2000, by determining the spatially and temporally aggregated land surface influence on deep convection from composites of thermodynamic variables [e.g., surface lifted index (SLI), level of free convection (LFC), and lifted condensation level (LCL)] that are obtained from mapped data of the 6-h NCEP–NCAR reanalyses (NNR), and 0000 UTC rawinsonde ascents; and 2) for summer seasons 1995–2001, from the statistical associations of satellite-retrieved LULC boundary attributes (i.e., length and width) and precipitation at high spatial resolutions. For the 1999 and 2000 summers (item 1 above), thermodynamic composites determined for V(500) categories having minimal differences in synoptic meteorological fields on CV minus NC (CV − NC) days (i.e., weaker flow), show statistically significant increases in atmospheric moisture (e.g., greater precipitable water; lower LCL and LFC) and static instability [e.g., positive convective available potential energy (CAPE)] compared to NC days. Moreover, CV days for both weaker and stronger background flow have associated subregional-scale thermodynamic patterns indicating free convection at the earth’s surface, supported by a synoptic pattern of at least weakly upward motion of air in the midtroposphere in contrast to NC days. The possibility that aerodynamic contrasts along QPVBs readily permit air to be lofted above the LFC when the lower atmosphere is moist, thereby assisting or enhancing deep convection on CV days, is supported by the multiyear analysis (item 2 above). In early summer when LULC boundaries are most evident, precipitation on weaker flow days is significantly greater within 20 km of boundaries than farther away, but there is no statistical difference on stronger flow days. Statistical relationships between boundary mean attributes and mean precipitation change sign between early summer (positive) and late summer (negative), in accord with shifts in the satellite-retrieved maximum radiances from forest to crop areas. These phenological changes appear related, primarily, to contrasting soil moisture and implied evapotranspiration differences. Incorporating LULC boundary locations and phenological status into reliable forecast fields of lower-to-midtropospheric humidity and wind speed should lead to improved short-term predictions of convective precipitation in the Corn Belt and also, potentially, better climate seasonal forecasts.


2013 ◽  
Vol 13 (11) ◽  
pp. 29137-29201 ◽  
Author(s):  
B. P. Guillod ◽  
B. Orlowsky ◽  
D. Miralles ◽  
A. J. Teuling ◽  
P. Blanken ◽  
...  

Abstract. The feedback between soil moisture and precipitation has long been a topic of interest due to its potential for improving weather and seasonal forecasts. The generally proposed mechanism assumes a control of soil moisture on precipitation via the partitioning of the surface turbulent heat fluxes, as assessed via the Evaporative Fraction, EF, i.e. the ratio of latent heat to the sum of latent and sensible heat, in particular under convective conditions. Our study investigates the poorly understood link between EF and precipitation by investigating the impact of before-noon EF on the frequency of afternoon precipitation over the contiguous US, using a statistical analysis of the relationship between multiple datasets of EF and precipitation. We analyze remote sensing data products (EF from GLEAM, Global Land Evaporation: the Amsterdam Methodology, based on satellite observations; and radar precipitation from NEXRAD, the NEXt generation weather RADar system), FLUXNET station data, and the North American Regional Reanalysis (NARR). While most datasets agree on the existence of regions of positive relationship between between EF and precipitation in the Eastern and Southwestern US, observation-based estimates (GLEAM, NEXRAD and to some extent FLUXNET) also indicate a strong relationship in the Central US which is not found in NARR. Investigating these differences, we find that much of these relationships can be explained by precipitation persistence alone, with ambiguous results on the additional role of EF in causing afternoon precipitation. Regional analyses reveal contrasting mechanisms over different regions. Over the Eastern US, our analyses suggest that the apparent EF-precipitation coupling takes place on a short day-to-day time scale and is either atmospherically controlled (from precipitation persistence and potential evaporation) or driven by vegetation interception and subsequent re-evaporation (rather than soil moisture and related plant transpiration/bare soil evaporation), in line with the high forest cover and the wet regime of that region. Over the Central and Southwestern US, the impact of EF on convection triggering is additionally linked to soil moisture variations, owing to the soil moisture–limited climate regime.


Sign in / Sign up

Export Citation Format

Share Document