Tropical convection regimes in climate models: evaluation with satellite observations

Abstract

Abstract. High-quality observations are powerful tools for the evaluation of climate models towards improvement and reduction of uncertainty. Particularly at low latitudes, the most uncertain aspect lies in the representation of moist convection and interaction with dynamics, where rising motion is tied to deep convection and sinking motion to dry regimes. Since humidity is closely coupled with temperature feedbacks in the tropical troposphere, a proper representation of this region is essential. Here we demonstrate the evaluation of atmospheric climate models with satellite-based observations from Global Positioning System (GPS) radio occultation (RO), which feature high vertical resolution and accuracy in the troposphere to lower stratosphere. We focus on the representation of the vertical atmospheric structure in tropical convection regimes, defined by high updraft velocity over warm surfaces, and investigate atmospheric temperature and humidity profiles. Results reveal that some models do not fully capture convection regions, particularly over land, and only partly represent strong vertical wind classes. Models show large biases in tropical mean temperature of more than 4 K in the tropopause region and the lower stratosphere. Reasonable agreement with observations is given in mean specific humidity in the lower to mid-troposphere. In moist convection regions, models tend to underestimate moisture by 10 to 40 % over oceans, whereas in dry downdraft regions they overestimate moisture by 100 %. Our findings provide evidence that RO observations are a unique source of information, with a range of further atmospheric variables to be exploited, for the evaluation and advancement of nextgeneration climate models.

Consistency and structural uncertainty of multi-mission GPS radio occultation records

“…Also, new RO missions with advanced receivers will provide RO data with better quality. RO receivers are established on the Chinese FY-3 meteorological satellite series (Sun et al, 2018), Metop-C has been in orbit since November 2018, and the six-satellite FORMOSAT-7/COSMIC-2 constellation was successfully launched in June 2019 (Schreiner et al, 2016;Ho et al, 2019a).…”

Consistency and structural uncertainty of multi-mission GPS radio occultation records

“…RO observations improve weather prediction (Healy et al, 2005;Aparicio and Deblonde, 2008;Cardinali, 2009;Cucurull, 2010;Cardinali and Healy, 2014) and hurricane forecasts (e.g., Huang et al, 2005;Kuo et al, 2009;Liu et al 2012;Chen et al, 2015;. The RO data anchor atmospheric (re)analyses (Poli et al, 2010;Bauer et al, 2014;Simmons et al, 2017), and are useful for validating other types of observations (e.g., Steiner et al, 2007;He et al, 2009;Ladstädter et al, 2011;Ho et al, 2009a;2017;2018) and climate models (Ao et al, 2015;Pincus et al, 2017;Steiner et al, 2018).…”

Novel assessment of numerical forecasting model relative humidity with satellite probabilistic estimates

“…Assessing their accuracy is routinely performed by comparing the simulated geophysical fields to an observed reference derived from ground-based measurements or remote sensing techniques (Randall et al, 2007). When considering remote sensing techniques as reference, the comparison to numerical simulations may be performed in two way : (i) in the geophysical space, which means that the model geophysical variables are evaluated directly against remote sensing estimations based on a retrieval scheme, or (ii) in the observation (e.g., radiance) space, which means that a forward model is used to convert the simulated atmosphere into synthetic remote sensing measurements (Morcrette, 1991;Soden and Bretherton, 1994 ;Brogniez et al, 2005;Chepfer et al, 2008;Bodas-Salcedo et al, 2011;Jiang et al, 2012;Tian et al, 2013;Steiner et al, 2018).…”

“…The present work focuses on atmospheric relative humidity (RH). There is an extensive body of literature on the use of relative humidity estimated by space-borne instruments to evaluate climate models (among others Soden and Bretherton, 1994;Brogniez et al, 2005;John and Soden, 2006;Jiang et al, 2012;Tian et al, 2013;Steiner et al, 2018…). However, the comparison generally provides limited insight in their error characteristics for several reasons: 1.…”