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Hongxing Zheng
,
Francis H.S. Chiew
, and
Lu Zhang

Abstract

Dominant hydrological processes of a catchment could shift due to a changing climate. This climate-induced hydrological nonstationarity could affect the reliability of future runoff projection developed using a hydrological model calibrated for the historical period as the model or parameters may no longer be suitable under a different future hydroclimate. This paper explores whether competing parameterization approaches proposed to account for hydrological nonstationarity could improve the robustness of future runoff projection compared to the traditional approach where the model is calibrated targeting overall model performance over the entire historical period. The modeling experiments are carried out using climate and streamflow datasets from southeastern Australia, which has experienced a long drought and exhibited noticeable hydrological nonstationarity. The results show that robust multicriteria calibration based on the Pareto front can provide a more consistent model performance over contrasting hydroclimate conditions, but at a slight expense of increased bias over the entire historical period compared to the traditional approach. However, the robust calibration does not necessarily result in a more reliable projection of future runoff. This is because the systematic bias in any parameterization approach would propagate from the historical period to the future period and would largely be cancelled out when estimating the relative runoff change. Ensemble simulations combining results from different parameterization considerations could produce a more inclusive range of future runoff projection as it covers the uncertainties due to model parameterization.

Open access
Ryan Lagerquist
and
Imme Ebert-Uphoff

Abstract

In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise verification. However, neural networks (NN) in atmospheric science are almost always trained to optimize pixelwise loss functions, even when ultimately assessed with SV methods. This establishes a disconnect between model verification during versus after training. To address this issue, we develop spatially enhanced loss functions (SELF) and demonstrate their use for a real-world problem: predicting the occurrence of thunderstorms (henceforth, “convection”) with NNs. In each SELF we use either a neighborhood filter, which highlights convection at scales larger than a threshold, or a spectral filter (employing Fourier or wavelet decomposition), which is more flexible and highlights convection at scales between two thresholds. We use these filters to spatially enhance common verification scores, such as the Brier score. We train each NN with a different SELF and compare their performance at many scales of convection, from discrete storm cells to tropical cyclones. Among our many findings are that (i) for a low or high risk threshold, the ideal SELF focuses on small or large scales, respectively; (ii) models trained with a pixelwise loss function perform surprisingly well; and (iii) nevertheless, models trained with a spectral filter produce much better-calibrated probabilities than a pixelwise model. We provide a general guide to using SELFs, including technical challenges and the final Python code, as well as demonstrating their use for the convection problem. To our knowledge this is the most in-depth guide to SELFs in the geosciences.

Significance Statement

Gridded predictions, in which a quantity is predicted at every pixel in space, should be verified with spatially aware methods rather than pixel by pixel. Neural networks (NN), which are often used for gridded prediction, are trained to minimize an error value called the loss function. NN loss functions in atmospheric science are almost always pixelwise, which causes the predictions to miss rare events and contain unrealistic spatial patterns. We use spatial filters to enhance NN loss functions, and we test our novel spatially enhanced loss functions (SELF) on thunderstorm prediction. We find that different SELFs work better for different scales (i.e., different-sized thunderstorm complexes) and that spectral filters, one of the two filter types, produce unexpectedly well calibrated thunderstorm probabilities.

Free access
Moritz Günther
,
Hauke Schmidt
,
Claudia Timmreck
, and
Matthew Toohey

Abstract

Volcanic aerosol forcing has previously been found to cause a weak global mean temperature response, as compared with CO2 radiative forcing of equal magnitude: its efficacy is supposedly low, but for reasons that are not fully understood. To investigate this, we perform idealized, time-invariant stratospheric sulfate aerosol forcing simulations with the MPI-ESM-1.2 and compare them with 0.5 × CO2 and 2 × CO2 runs. While the early decades of the aerosol forcing simulations are characterized by strong negative feedback (i.e., low efficacy), the feedback weakens on the decadal to centennial time scale. Although this effect is qualitatively also found in CO2-warming simulations, it is more pronounced for stratospheric aerosol forcing. The strong early and weak late cooling feedbacks compensate, leading to an equilibrium efficacy of approximately 1 in all simulations. The 0.5 × CO2 cooling simulations also exhibit strong feedback changes over time, albeit less than in the idealized aerosol forcing simulations. This suggests that the underlying cause for the feedback change is not exclusively specific to aerosol forcing. One critical region for the feedback differences between simulations with negative and positive radiative forcing is the tropical Indo-Pacific warm-pool region (30°S–30°N, 50°E–160°W). In the first decades of cooling, the temperature change in this region is stronger than the global average, whereas it is stronger outside it for 2 × CO2 warming. In cooling scenarios, this leads to an enhanced activation of the warm-pool region’s strongly negative lapse-rate feedback.

Significance Statement

Large volcanic eruptions can enhance the scattering aerosol layer in the stratosphere, which leads to a global cooling for a few years. Surprisingly, Earth has been found to cool less from radiative flux perturbations from stratospheric aerosol forcing, in comparison with how much it warms as a result of increases in CO2 concentration. We find that specific surface temperature change patterns after volcanic eruptions cause this effect. The temperature change in the tropical Indian and western Pacific Ocean determines how much global temperature change is needed to regain radiative equilibrium. Our findings contribute to understanding the climate response to volcanic eruptions and are relevant for understanding the mechanisms of climate change due to changes in CO2 concentration.

Restricted access
Xiaofeng Li
,
Dongliang Shen
,
Gang Zheng
,
Lizhang Zhou
, and
Aiyue Liu

Abstract

A winter storm triggered a significant sea smoke with the northwesterly wind to the Yellow Sea, China, on 7 January 2021. The ocean responses to this event lasted about 3 days. Satellite observations show that the sea surface temperature dropped from 5.7° to 4.7°C on the following day and then recovered to the previous level; the chlorophyll-a, a bio-growth indicator, increased from 3.6 to 3.9 mg m−3 due to cooling-induced coastal upwelling between 7 and 9 January. Two buoys measurements showed that the air temperature dropped to −13.3°C and high relative humidity with a maximum value of 89.0% above the sea surface, creating favorable conditions for sea smoke generation. A Regional Ocean Modeling System (ROMS) and Weather Research and Forecasting (WRF) Model coupled model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and analyze the air–sea interaction. The 20°C temperature difference between extreme cold air (−13.3°C) and the relatively warm stable sea surface (4.7°–5.7°C) enhanced the seawater evaporation. In addition, we suppose the concentration of sea salt, a kind of condensation nucleus, with a particle diameter of 0.5–1.5 μm above the sea surface increased quickly on 7 January. The boiling-water-like sea surface was imaged on a synthetic aperture image. We developed an image analysis method to describe the cell-shaped texture characteristics imaged by Synthetic Aperture Radar (SAR). We also found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability.

Significance Statement

On 7 January 2021, a significant sea smoke event happened in the Yellow Sea. The ocean response to the event lasted 3 days. First, on a synoptic scale, this study presents the comprehensive satellite observations of the sea surface temperature drop and chlorophyll-a increase associated with the sea smoke. Second, a coupled air–sea interaction model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and identify which condensation nucleus induced such heavy sea smoke. Third, we developed an image analysis method to analyze high-resolution synthetic aperture radar images and found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability.

Restricted access
Tianjiao Ma
,
Wen Chen
,
Shangfeng Chen
,
Chaim I. Garfinkel
,
Shuoyi Ding
,
Lei Song
,
Zhibo Li
,
Yulian Tang
,
Jingliang Huangfu
,
Hainan Gong
, and
Wei Zhao

Abstract

This study aims to better understand the ENSO impacts on climate anomalies over East Asia in early winter (November–December) and late winter (January–February). In particular, the possible mechanisms during early winter are investigated. The results show that ENSO is associated with a Rossby wave train emanating from the tropical Indian Ocean toward East Asia (denoted as tIO-EA) in early winter. This tIO-EA wave train in El Niño (La Niña) is closely related to a weakening (strengthening) of the East Asian trough, and thereby a weakened (strengthened) East Asian winter monsoon and warm (cold) temperature anomalies over northeastern China and Japan. By using partial regression analysis and numerical experiments, we identify that the formation of tIO-EA wave train is closely related to precipitation anomalies in the tropical eastern Indian Ocean and western Pacific (denoted as eIO/wP). In addition, the ENSO-induced North Atlantic anomalies may also contribute to formation of the tIO-EA wave train in conjunction with the eIO/wP precipitation. The response of eIO/wP precipitation to ENSO is stronger in early winter than in late winter. This can be attributed to the stronger anomalous Walker circulation over the Indian Ocean, which in turn is caused by higher climatological SST and stronger mean precipitation state in the Indian Ocean during early winter.

Restricted access
Ophélia Miralles
,
Daniel Steinfield
,
Olivia Martius
, and
Anthony C. Davison

Abstract

Near-surface wind is difficult to estimate using global numerical weather and climate models, because airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach based on deep learning and a high-resolution digital elevation model to spatially downscale hourly near-surface wind fields at coarse resolution from ERA5 reanalysis from their original 25-km grid to a 1.1-km grid. A 1.1-km-resolution wind dataset for 2016–20 from the operational numerical weather prediction model COSMO-1 of the national weather service MeteoSwiss is used to train and validate our model, a generative adversarial network (GAN) with gradient penalized Wasserstein loss aided by transfer learning. The results are realistic-looking high-resolution historical maps of gridded hourly wind fields over Switzerland and very good and robust predictions of the aggregated wind speed distribution. Regionally averaged image-specific metrics show a clear improvement in prediction relative to ERA5, with skill measures generally better for locations over the flatter Swiss Plateau than for Alpine regions. The downscaled wind fields demonstrate higher-resolution, physically plausible orographic effects, such as ridge acceleration and sheltering, that are not resolved in the original ERA5 fields.

Significance Statement

Statistical downscaling, which increases the resolution of atmospheric fields, is widely used to refine the outputs of global reanalysis and climate models, most commonly for temperature and precipitation. Near-surface winds are strongly modified by the underlying topography, generating local flow conditions that can be very difficult to estimate. This study develops a deep learning model that uses local topographic information to spatially downscale hourly near-surface winds from their original 25-km resolution to a 1.1-km grid over Switzerland. Our model produces realistic high-resolution gridded wind fields with expected orographic effects but performs better in flatter regions than in mountains. These downscaled fields are useful for impact assessment and decision-making in regions where global reanalysis data at coarse resolution may be the only products available.

Free access
Boyin Huang
,
Xungang Yin
,
Matthew J. Menne
,
Russell Vose
, and
Huai-Min Zhang

Abstract

NOAA global surface temperature (NOAAGlobalTemp) is NOAA’s operational global surface temperature product, which has been widely used in Earth’s climate assessment and monitoring. To improve the spatial interpolation of monthly land surface air temperatures (LSATs) in NOAAGlobalTemp from 1850 to 2020, a three-layer artificial neural network (ANN) system was designed. The ANN system was trained by repeatedly randomly selecting 90% of the LSATs from ERA5 (1950–2019) and validating with the remaining 10%. Validations show clear improvements of ANN over the original empirical orthogonal teleconnection (EOT) method: the global spatial correlation coefficient (SCC) increases from 65% to 80%, and the global root-mean-square difference (RMSD) decreases from 0.99° to 0.57°C during 1850–2020. The improvements of SCCs and RMSDs are larger in the Southern Hemisphere than in the Northern Hemisphere and are larger before the 1950s and where observations are sparse. The ANN system was finally fed in observed LSATs, and its output over the global land surface was compared with those from the EOT method. Comparisons demonstrate similar improvements by ANN over the EOT method: The global SCC increased from 78% to 89%, the global RMSD decreased from 0.93° to 0.68°C, and the LSAT variability quantified by the monthly standard deviation (STD) increases from 1.16° to 1.41°C during 1850–2020. While the SCC, RMSD, and STD at the monthly time scale have been improved, long-term trends remain largely unchanged because the low-frequency component of LSAT in ANN is identical to that in the EOT approach.

Significance Statement

The spatial interpolation method of an artificial neural network has greatly improved the accuracy of land surface air temperature reconstruction, which reduces root-mean-square error and increases spatial coherence and variabilities over the global land surface from 1850 to 2020.

Free access
Xiong Zhou
,
Guohe Huang
,
Yurui Fan
,
Xiuquan Wang
, and
Yongping Li

Abstract

Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow.

Significance Statement

Increasing concerns have been paid to climate change due to its aggravating impacts on the hydrologic regime, leading to water-related disasters. Such impacts can be investigated through long-term hydrological projection under climate change. However, it is not well understood what factor plays a dominant role in inducing extensive uncertainties associated with the long-term hydrological projections due to plausible meteorological forcings, multiple hydrologic models, and internal variability. The stepwise cluster Bayesian ensemble method and mixed-level factorial inference approach are employed to quantify the contribution of multiple uncertainty sources. We find that the total variance of changes in monthly precipitation, potential evapotranspiration, and streamflow can be mainly explained by the model choices. The identified dominant factor accounting for projection uncertainties is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management. It is suggested that more reliable models should be taken into consideration in order to improve the projection robustness from a perspective of the Loess Plateau.

Restricted access
Viktor Gouretski
,
Lijing Cheng
, and
Tim Boyer

Abstract

Nansen bottle casts served as the main oceanographic instrumentation type for more than a century since the establishing of the technique in the late 1890s. Between the end of the 1960s and the end of the 1990s Nansen cast technique has been gradually replaced by electronic sensor profilers (CTD). Both instrumentation types are considered as the most accurate among other oceanographic instruments and are often used as the unbiased reference. We conducted a comprehensive investigation of the consistency of the temperature data from Nansen casts and CTD profilers analyzing the quasi-collocated bottle and CTD data between the 1960s and the 1990s when both instrumentation types overlap. We found that Nansen casts tend to overestimate the sample depth with reversing mercury-in-glass thermometer temperatures being on average slightly lower compared to CTD data. Respectively, depth and temperature corrections are provided. Further, we estimated the ocean heat content changes between 1955 and 1990 using (along with all other instrumentation types) corrected and uncorrected Nansen cast data. These calculations show that for the upper 2 km layer the global average warming trend for this time period increases from 0.20 ± 0.05 W m−2 for the uncorrected data to 0.28 ± 0.06 W m−2 for the corrected data at the 90% confidence level. Finally, we suggest that the Nansen bottle cast profiles be put into a separate instrumentation group within the World Ocean Database.

Restricted access
Zijin Zhang
,
Xiaolong Dong
, and
Di Zhu

Abstract

The O2-band channel configuration of existing microwave radiometers is not optimal for surface pressure retrieval, which limits the surface pressure retrieval accuracy. In this study, we present the results of theoretically what might be the optimal microwave channels for surface pressure retrieval. An improved iterative selection method is used to select the channels that contain the highest cumulative content of surface pressure information. The selected optimal channel set comprises 16 channels, among which 10 channels are centered at the 50–60 GHz oxygen absorption band and 6 channels are centered around the 118.75 GHz oxygen absorption line. Two representative spaceborne microwave radiometers are used for comparisons, the Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite and the Microwave Humidity and Temperature Sounder (MWHTS) on board the Chinese Fengyun-3C (FY-3C) satellite. The results of information content analysis show that the optimal channel set contains more surface pressure information than that of the combination of SNPP/ATMS and FY-3C/MWHTS (SNPP/ATMS+FY-3C/MWHTS) channels. A representative dataset from the ERA5 data is input into the plane-parallel Microwave Radiative Transfer model to obtain the simulated brightness temperature observations of the selected optimal channels and the SNPP/ATMS+FY-3C/MWHTS channels. Using the simulated observations, retrieval experiments are performed. Experimental results show that retrieval accuracies of the optimal channel set are 1.09 and 1.64 hPa for clear-sky and cloudy conditions, respectively. The retrieval accuracies are 0.60 and 0.65 hPa better than that of the SNPP/ATMS+FY-3C/MWHTS channels for clear-sky and cloudy conditions, respectively.

Restricted access