Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from ∼1∘ (eddy-parametrized) to ∼0.25∘ (eddy-present). For one model, ocean resolution also reaches 1/12∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary.

Subsurface warm biases in the tropical Atlantic and their attributions to the role of wind forcing and ocean vertical mixing

Realistic ocean subsurface simulations of thermal structure and variation are critically important to the success in climate prediction and projection; currently, substantial systematic subsurface biases still exist in the state-of-the-art ocean and climate models. In this paper, subsurface biases in the tropical Atlantic (TA) are investigated by analyzing simulations from OMIP and conducting POP2-based ocean-only experiments. The subsurface biases are prominent in almost all OMIP simulations, characterized by two warm bias patches off the equator. By conducting two groups of POP2-based ocean-only experiments, two potential origins of the biases are explored, including uncertainties in wind forcing and vertical mixing parameterization, respectively. It is illustrated that the warm bias near 10° N can be slightly reduced by modulating prescribed wind field, and the warm biases over the entire basin are significantly reduced by reducing background diffusivity in the ocean interior in ways to match observations. By conducting heat budget analysis, it is found that the improved subsurface simulations are attributed to the enhanced cooling effect by constraining the vertical mixing diffusivity in terms of the observational estimate, implying that the overestimation of vertical mixing is primarily responsible for the subsurface warm biases in the TA. Since the climate simulation is very sensitive to the vertical mixing parameterization, more accurate representations of ocean vertical mixing are clearly needed in ocean and climate models.

Sensitivity of Tropical Tropopause Layer Cirrus Prediction in GRAPES Global Forecast System

A warm bias with a maximum value of over 4 K in the tropical tropopause layer (TTL) is detected in day-5 operational forecasts of the Global/Regional Assimilation and Prediction System (GRAPES) for global medium-range numerical weather prediction (GRAPES_GFS). In this study, the predicted temperature changes caused by different processes are examined, and the predicted cloud fractions are compared with the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data. It is found that the overprediction of the TTL cirrus fraction contributes to the warm bias due to cloud-radiative heating. The interactions among the ice nucleation, deposition/sublimation, and the large-scale condensation together determine the results of the TTL ice crystal content prediction. Moreover, a range of sensitivity experiments show that the TTL ice crystal content prediction is sensitive to the threshold relative humidity over ice (RHi) in the ice nucleation process. Then the uncertainties of the formulas for saturation vapor pressure over ice at very low temperatures are discussed. The RHi calculated based on the Magnus–Tetens formula is up to 10% higher than that based on the Goff–Gratch formula. As the Goff–Gratch formula is applicable over a broader range of 184–273 K, it is more suitable for the cold TTL. When the Goff–Gratch formula rather than the Magnus–Tetens formula is used in the microphysics scheme, the TTL cirrus forecasts are improved greatly, and the warm bias disappears completely. After investigating the interplay of the dynamical, microphysical, and radiative processes, we find a positive feedback mechanism that exacerbates the TTL cirrus prediction error.

Disentangling the effect of regional SST bias on the double-ITCZ problem

This study investigates the causes of the double intertropical convergence zone (ITCZ) bias by disentangling the individual contribution of regional sea surface temperature (SST) biases. We show that a previously suggested Southern Ocean warm bias effect in displacing the zonal-mean ITCZ southward is diminished by the southern midlatitude cold bias effect. The northern extratropical cold bias turns out to be most responsible for a southward-displaced zonal-mean precipitation, but the zonal-mean diagnostics poorly represent the spatial pattern of the tropical Pacific response. Examination of longitude-latitude structure indicates that the overall spatial pattern of tropical precipitation bias is largely shaped by the local SST bias. The southeastern tropical Pacific wet bias is driven by warm bias along the west coast of South America with negligible influence from the Southern Ocean warm bias. While our model experiments are idealized with ocean dynamics being absent, the results shed light on where preferential foci should be applied in model development to improve the certain features of tropical precipitation bias.

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

We examine the impacts of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. Atmospheric resolution is increased from ~100–200 km to ~25–50 km, and ocean resolution is increased from ~1° (i.e., eddy-parametrized) to ~0.25° (i.e., eddy-present). For one model, ocean resolution is also increased to 1/12° (i.e., eddy-rich). Fully-coupled general circulation models and their atmosphere-only versions are compared with observations and reanalysis of near-surface temperature, precipitation, cloud cover, net cloud radiative effect, and zonal wind over the period 1980–2014. Both the ensemble mean and individual models are analyzed. Increased resolution especially in the atmosphere helps reduce the surface warm bias over the tropical upwelling regions in the coupled models, with further improvements in the cloud cover and precipitation biases particularly over the tropical South Atlantic. Related to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double Intertropical Convergence Zone bias also weakens with resolution. Overall, increased ocean resolution from ~1° to ~0.25° offers limited improvements or even bias degradation in some models, although an eddy-rich ocean resolution seems beneficial for reducing the biases in North Atlantic temperatures and Gulf Stream path. Despite the improvements, however, large biases in precipitation and cloud cover persist over the whole tropics as well as in the upper-troposphere zonal winds at mid-latitudes in coupled and atmosphere-only models at higher resolutions. The Southern Ocean warm bias also worsens or persists in some coupled models. And a new warm bias emerges in the Labrador Sea in all the high-resolution coupled models. The analysis of the PRIMAVERA models therefore suggests that, to reduce biases, i) increased atmosphere resolution up to ~25–50 km alone might not be sufficient and ii) an eddy-rich ocean resolution might be needed. The study thus adds to evidence that further improved model physics and tuning might be necessary in addition to increased resolution to mitigate biases.

Comparison of GRUAN RS92 and RS41 Radiosonde Temperature Biases

In this study, we validated the consistency of the GRUAN RS92 and RS41 datasets, versions EDT.1 and GDP.2, in the upper troposphere and lower stratosphere (200–20 hPa), through dual launch campaigns at the GRUAN site and using the radio occultation (RO) product and the ERA5 reanalysis from ECMWF as standards for double difference comparison. Separate comparisons with the references were also performed in order to trace the origin of the bias between the two instruments. Then, the performance of the GRUAN raw temperature correction algorithm was evaluated, from the aspects of day–night, the solar zenith angle, and the pressure level, for GDP.2 version products. The results show that RS92.EDT.1 has a warm bias of 0.355 K, compared to RS41.EDT.1, at 20 hPa, during daytime. This bias was found to mainly originate from RS92.EDT.1, based on the separate comparison with RO or ECMWF ERA5 data. RS92.GDP.2 is consistent with RS41.GDP.2, but a separate comparison indicated that the two original GDP.2 products have a ~1 K warm bias at 20 hPa during daytime, compared with RO or ECMWF ERA5 data. The GRUAN correction method can reduce the warm bias up to 0.5 K at 20 hPa during daytime. As a result, this GRUAN correction method is efficient, and it is dependent on the solar zenith angle and pressure level.

Late Spring and Summer Subseasonal forecasts in the Northern Hemisphere midlatitudes: biases and skill in the ECMWF model

Subseasonal forecasts lie between medium-range and seasonal time scales with an emerging attention due to the relevance in society and by the scientific challenges involved. This study aims to (i) evaluate the development of systematic errors with lead time in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of surface-related variables during late spring and summer, and (ii) investigate potential relationships between the systematic errors and predictive skill. The evaluation is performed over the northern hemisphere midlatitudes, focusing on several regions with different climate characteristics. The results indicate five key bias patterns: (i) cold bias of daily maximum temperature (mx2t) in the April-May forecasts at all lead times in most regions; (ii) central North America with a warm bias mostly in the daily minimum temperature (mn2t); (iii) East of Caspian Sea region with a warm and dry bias; (iv) Western and Mediterranean Europe with a cold bias in mn2t mainly in April-May forecasts and (v) continental Europe with a cold bias in the mx2t and warm bias of mn2t in the June-July forecasts. We also found substantial deviations of soil moisture and terrestrial water storage variation in most regions compared to ECMWF ERA5 reanalysis. Despite the large differences in the systematic error characteristics among the different regions, there is little relation to the skill of the subseasonal forecasts. The systematic temperature biases require further attention from model developers as diurnal cycle improvements could enhance some of the potential predictability coming from the long-memory effect of soil moisture.

Characterizing and correcting the warm bias observed in AMDAR temperature observations

Some aircraft temperature observations, retrieved through the Aircraft Meteorological Data Relay (AMDAR), suffer from a significant warm bias when comparing observations with numerical weather prediction (NWP) model. In this manuscript we show that this warm bias of AMDAR temperature can be characterized and consequently reduced substantially. The characterization of this warm bias is based on the methodology of measuring temperature with a moving sensor and can be split into two separate processes. The first process depends on the flight phase of the aircraft and relates to difference of timing, as it appears that the time of measurement of altitude and temperature differ. When an aircraft is ascending or descending this will result in small bias in temperature due to the (on average) presence of an atmospheric temperature lapse rate. The second process is related to internal corrections applied to pressure altitude without feedback to temperature observation measurement. Based on NWP model temperature data combined with additional information on Mach number and true airspeed, we were able to estimate corrections using an 18 months period from January 2017 to July 2018. Next, the corrections were applied on AMDAR observations over the period from September 2018 to mid-December 2019. Comparing these corrected temperatures with (independent) radiosonde temperature observations demonstrates a reduction of the temperature bias from 0.5 K to around zero and reduction of standard deviation of almost 10 %.

Underestimated snow density in ERA5-Land as a cause for warm bias of soil temperature over permafrost regions

<div> <p>ERA5 is the latest generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5-Land (ERA5L) is derived by running the land component of ERA5, Tiled ECMWF Scheme for Surface Exchanges over Land with a revised land surface hydrology (HTESSEL), at an increased resolution of 0.1°. This study evaluates ERA5L soil temperature in permafrost regions based on observations and published permafrost products. We find that ERA5L overestimates soil temperature in northern Canada and Alaska but underestimates it in mid–low latitudes, leading to a near-zero overall bias (−0.08 ˚C). The warm bias of ERA5L soil is more pronounced in winter than in other seasons. As calculated from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (< 1.89 m) permafrost area.This is thought to be due in part to the shallow soil column and coarse vertical discretization of the land surface model and to warmer simulated soil.</p> <p>The soil temperature bias in permafrost regions correlates well with the bias in air temperature and with snow height. A review of the ERA5L snow parameterization in the code and a simulation example comparison with permafrost-specific processes rich model (GEOtop) both point to an error in snow metamorphism of HTESSEL leading to a low bias in ERA5L snow density as a possible cause for the warm bias in soil temperature. The apparent disagreement of station-based and areal evaluation techniques highlights challenges in our ability to test permafrost simulation models. While global reanalyses are important drivers for permafrost simulation, we conclude that ERA5L soil data are not well suited for informing permafrost research and decision making directly. To address this, future soil temperature products in reanalyses will require permafrost-specific alterations to their land surface models.</p> <p></p></div>