1. Introduction
Ocean in situ temperature observations show increasing ocean temperatures (e.g., Lyman and Johnson 2014; Cheng et al. 2017), which is the primary manifestation of Earth’s energy imbalance (EEI) derived from top-of-atmosphere (TOA) radiation (von Schuckmann et al. 2016). Ocean temperature changes are driven by increasing anthropogenic forcing, associated with changes in anthropogenic aerosols and greenhouse gases, which in turn drive changes in the flows of energy through the climate system. Ultimately these perturbed flows are known to sequester over 90% of EEI in the ocean. However, our understanding of the connections between perturbed TOA irradiances, changes in the forms and flows of energy within the atmosphere, and perturbed air–sea exchanges driving ocean warming remain limited. Adequately tracking and accounting for these perturbed flows has been a major challenge for existing observing systems, as uncertainties in energy flow components, particularly at the surface and within the atmosphere, can exceed estimated perturbations driven by climate change by an order of magnitude. This is evident, for example, when satellite-derived products are used to close the surface energy budget, as there is a significant discrepancy in the annual global energy balance of 10 to 15 W m−2 (Kato et al. 2011; L’Ecuyer et al. 2015). While the cause of the residual is unknown, Loeb et al. (2014) and Kato et al. (2016) used satellite-derived radiation, precipitation, sensible heat flux, and atmospheric energy transport data products to identify the tropical ocean as a key region where large discrepancies occur. In addition to the problem of understanding surface radiation budget, the discrepancy of 10 to 15 W m−2 creates a significant problem in evaluating climate models with satellite data products. When models are constrained by TOA radiation data products, models that conserve energy and water mass do not match an observed global mean precipitation rate (e.g., Held et al. 2019) because of, in part, the existence of a significant energy balance residual when satellite energy flux products are integrated.
While the total energy is conserved, energy is converted and transferred in various different forms in the atmosphere. Although the regional energy balance in the atmosphere is largely achieved with diabatic heating by precipitation, radiative cooling, and dry static energy divergence by dynamics (Trenberth and Stepaniak 2003a; Kato et al. 2016), all forms need to be considered in closing the energy budget in the atmosphere. Identifying assumptions made in producing data products and accounting for all energy fluxes are the first steps toward closing the regional energy budget when regional energy balance in the atmosphere and surface is analyzed by integrating multiple data products. To include all energy fluxes and identify assumptions that arise when multiple data products are integrated, we formulate an improved set of energy budget equations in this study, aiming toward balancing the energy budget through integrating observational energy flux data products.
Energy budget equations derived in earlier studies (e.g., Peixoto and Oort 1992; Trenberth 1997; Trenberth and Stepaniak 2003b) consider moist air but do not express hydrometeors that may have different velocities from the moist air velocity (e.g., precipitation) explicitly. In addition, equations used in earlier studies mostly do not make a distinction between the liquid and ice water phases of hydrometeors. Furthermore, temperature dependence of the enthalpy of vaporization is often ignored in computing global energy budgets (e.g., Trenberth et al. 2009; Stephens et al. 2012). The enthalpy transfer associated with water mass transfer (Mayer et al. 2017; Trenberth and Fasullo 2018) also depends on the phase and temperature of hydrometeors. Assumptions made in formulating earlier energy budget equations are understandable because some of the variables were (and still are) not available as outputs from reanalysis or data products to analyze or diagnose the energy balance. However, that does not preclude work identifying missing energy fluxes and assumptions. Additionally, uncertainties arise when integrating multiple energy data products, especially in converting precipitation rate to diabatic heating rate. In this study, we quantify the effect of temperature dependence on thermodynamic constants and the phase of precipitation when computing diabatic heating rates.
The improved atmospheric energy budget equations developed in this study explicitly account for water vapor, ice, liquid clouds, and precipitation. We use a framework to separate water variables from dry air and to aid identifying assumptions made when multiple energy flux data products are integrated. Separation of precipitations from dry air is necessary to explicitly accounting for their different vertical velocities from dry-air velocity. Although explicitly treating dry air, water vapor, and hydrometeors has been done in earlier work (e.g., Bannon 2002; Satoh 2003), regional energy equations integrated over the atmospheric column explicitly treating dry air, water vapor, and hydrometeors were not derived in earlier studies. We use the velocity of dry air as the reference velocity instead of barycentric velocity and express water vapor and hydrometeor velocities relative to this reference velocity, because dry-air velocity is independent of hydrometeor fallout as precipitation. In addition, because dry-air mass is conserved, we use the dry-air mass as the vertical coordinate instead of total pressure, in accordance with the recent development of National Center for Atmospheric Research Community Earth System Model (Lauritzen et al. 2018). As stated by Lauritzen et al. (2018), the use of dry-air reference velocity and dry-air mass coordinates has an advantage for our purpose. The conservation of energy associated with water is visible in the derivation because all water variables are separated from dry air. We then use improved energy equations to assess the effect of horizontal transport of hydrometeors and enthalpy transport associated with precipitation on the atmospheric energy budget.
Energy and water budget equations are formulated in section 2. Water mass balance and the effect of water mass balance residual and horizontal transport of hydrometeors are assessed in section 3. Enthalpy fluxes at the surface due to water mass fluxes are estimated in section 4a. The effects of water phase and temperature dependence of thermodynamic constants on diabatic heating rates are evaluated in section 4b. Section 5 summarizes the results. Energy and water budget equations derived in section 2 can be applied to an atmospheric column over ocean and land. However, because the surface latent heat flux product used in section 4 includes fluxes only for ocean surfaces, our focus, and the analysis of the enthalpy flux associated with water mass transfer, is limited to over the ocean. Because both turbulent fluxes and energy fluxes associated with mass transfer are all enthalpy fluxes, we refer to them collectively as the enthalpy flux. Therefore, we use surface sensible and latent heat fluxes, as they are generally used, to distinguish them from the enthalpy flux associated with water mass transfer in this paper.
2. Atmospheric energy and water budget equations
The energy budget equations presented below are similar to those used in earlier studies (e.g., Peixoto and Oort 1992; Trenberth 1997). Although column integrated atmospheric energy equations do not depend on the choice of the vertical coordinate system, revised equations are formulated in a dry-air mass coordinate. Revisions also include the formulation of energy storage per unit mass of dry air instead of moist air as it is the former that is strictly conserved. We treat dry air and water vapor as independent components of a mixture. Furthermore, we explicitly distinguish between liquid and ice cloud particles in our accounting of moisture storage and precipitation. Our derivation follows the derivation of moist convection equations formulated by Bannon (2002). However, an important difference is that we assume hydrostatic balance in our derivation as a reasonable assumption for the horizontal and temporal scales for which the equations are applied. We also assume that dry air, water vapor, and hydrometeors in the parcel are all at the same temperature. In addition, we ignore the volume occupied by hydrometeors.
The atmospheric energy budget equation that uses moist static energy and energy fluxes at boundaries is provided in appendix D [Eq. (D5)]. Two forms of atmospheric energy equations are used in section 3 to assess the effect of the transport of hydrometeors on the regional atmospheric energy budget.
3. Evaluation of water mass balance and the effect of hydrometeor horizontal advection
Equations (38) and (D5) include horizontal enthalpy fluxes associated with horizontal transport of hydrometeors. In the analysis by Kato et al. (2016), the horizontal transport of hydrometeors is neglected. In addition, all precipitation is assumed to be formed in the atmospheric column where fallout occurs. Furthermore, the water mass is not necessarily conserved when flux data products are integrated. We evaluate regional water mass balance and the impact of horizontal transport of hydrometers on regional energy budget in this section.
To assess the water mass balance in the atmospheric column with the data products used in the study by Kato et al. (2016), we compute regional precipitation rate
To illustrate the potential impact of the horizontal transport of hydrometeors (e.g., ice crystals) on the energy budget of an atmospheric column and to assess whether the transport explains a part of the regional energy budget residual, we consider three extreme cases shown schematically in Fig. 2. In each case water vapor is transported into the atmospheric column in which ice crystals are present. In case 1, all water vapor is transported out of the column. In cases 2 and 3, all water vapor is condensed, freezes, and ice crystals are formed. In case 2, all ice crystals are transported out of the column while in case 3 the ice crystals melt and fallout. The reverse process of case 2 can occur somewhere else. All three cases are in a steady state. Table 1 summarizes the dry static energy, moist static energy and diabatic heating for these three cases using Eqs. (38) and (D5). Vertical integration and kinetic energy are omitted in the expression shown in Table 1. Differences in diabatic heating rate, dry static energy convergence, moist static energy convergence, and Ffallout between these cases are summarized in Table 2.
Dry and moist static energy advection and diabatic heating associated with hydrometeor transport. Asterisks (*) indicate that vertical integration and kinetic energy are omitted in the expression.
Difference of diabatic heating and dry and moist static energies.
The largest difference of diabatic heating rate
In summary, a part of regional energy balance residual shown in Kato et al. (2016) is due to regional water mass imbalance. If a larger water vapor convergence that is needed to reduce the water mass balance residual over tropical western pacific and the additional convergence is associated with diabatic heating by condensation or freezing (e.g., a combination of cases 2 and 3), then the additional water vapor convergence also is to increase diabatic heating in the atmospheric column. Considering this process that is missing in Kato et al. (2016), therefore, can reduce regional water mass and energy balance residuals. The horizontal transport of hydrometeors, however, only affects regional energy budget and does not contribute to the global energy budget residual.
4. Enthalpy fluxes associated with data products integration
There are energy fluxes that are not included in an individual data product but arise when multiple energy flux data products are integrated. The terms Fυ and Ffallout in Eqs. (38) and (D5) are generally not included in turbulence flux data products. We estimate these fluxes in section 4a. In addition, computing diabatic heating terms in Eq. (38) requires the knowledge of the phase and temperature of hydrometeors (e.g., height where condensation occurs) (e.g., Shige et al. 2004; Tao et al. 2006; Nelson et al. 2016). In section 4b, we investigate the effect of temperature dependence of the enthalpy of vaporization to diabatic heating estimates.
a. Energy transfer associated with mass transfer
Equations (38) and (D5) contain the horizontal enthalpy flux and enthalpy flux associated with precipitation Ffallout and evaporation Fυ. As pointed out by Mayer et al. (2017), the reference temperature needs to be defined to compute enthalpy fluxes. Mayer et al. (2017) and Trenberth and Fasullo (2018) use 0°C as a reference temperature. However, when the mass-weighted global mean ocean or atmosphere temperature is used as the reference temperature, the flux divided by the heat capacity of ocean or atmosphere is the temperature change (appendix E). In this section, we estimate Ffallout and Fυ using mass-weighted ocean and atmosphere temperature as the reference temperature. As pointed out by Mayer et al. (2017), however, the same reference temperature for the atmosphere and ocean is required when the energy budget of the atmosphere and ocean combined system is considered. Moving water between the atmosphere and ocean should not alter the energy budget of the ocean and atmosphere combined system. For practical purposes, therefore, the use of 0°C as a reference temperature for both ocean and atmosphere in computing the enthalpy flux associated with water mass transfer is a reasonable compromise (Mayer et al. 2017; Trenberth and Fasullo 2018).
While not significantly different from 0°C, the global mean mass-weighted ocean mean temperature is 3.7°C. This value is from the World Ocean Atlas (WOA) 2013 V2 ocean atlas climatology product (Locarnini et al. 2013) and includes the entire ocean depth. In the ocean mean temperature estimate, the mass is assumed to be independent of depth and salinity dependence is ignored (i.e., the mass-weighted mean is therefore equal to the volume weighted mean). To derive the mass-weighted global mean temperature of the atmosphere we use tropical and midlatitude summer standard atmospheres. For both atmospheres, the mass-weighted mean temperature is close to 450-hPa air temperature. Therefore, we use the global mean 450-hPa air temperature of 255 K for
To understand the spatial distribution of the enthalpy fluxes, Fig. 3 shows
b. Diabatic heating rate in the atmosphere by precipitation
As indicated by Eq. (38), the phase of hydrometeors is needed to compute diabatic heating rate due to precipitation. In addition, the temperature at which precipitation is formed is different from a 2-m wet-bulb temperature, our assumed hydrometeor temperature in section 3a, when it falls to the ground. Therefore, the enthalpy transferred to or from hydrometeors to make their temperature a 2-m wet-bulb temperature after they form needs to be considered. Because a 2-m wet-bulb temperature is generally higher than a temperature at which precipitation is formed, the diabatic heating rate due to precipitation is expected to be smaller when the enthalpy transport to the hydrometeors is included.
When warm rain forms at the 750-hPa level and falls toward a surface with the 2-m wet-bulb temperature, the enthalpy loss by the atmosphere to warm liquid water from T750 to Tskin is cl(Tskin − T750), where cl(Tw − T750) is transferred in the atmosphere and cl(Tskin − Tw) at the surface. When water vapor evaporates at the surface in the same column, and condenses at the 750-hPa level, the enthalpy transferred to the atmosphere to cool water vapor from Tskin to T750 is
Figure 4 (left) shows regional diabatic heating rates due to precipitation including the enthalpy transport to hydrometeors discussed above. Four different types of precipitation explained in appendix F are considered. The global annual mean diabatic heating rate by precipitation is 78.9 W m−2 with the 750-hPa threshold temperature to determine the phase equal to −10°C, which is 0.8 W m−2 larger than the value computed with a constant enthalpy of vaporization at 0°C (i.e., 2.5 × 10−6 J kg−1). The regional annual mean difference can be larger than 15 W m−2 as shown in the right plot of Fig. 4 (right). When the 750-hPa threshold temperature is changed to −5°C (−25°C), the global annual mean enthalpy release by precipitation is 79.7 W m−2 (78.1 W m−2). The differences in the global annual mean are within ±1 W m−2. However, GPCP precipitation rate might not include some snow precipitation, which can be up to 4 W m−2 (Stephens et al. 2012).
Even though we separated diabatic heating computations in four cases, the diabatic heating rate in the atmosphere largely depends on the water phase of hydrometers that fall on the surface. As a consequence, for a given rain rate, diabatic heating for Precip. 2 and 4 is larger than diabatic heating for Precip. 1 and 3. For Precip. 2 and 4, when snow melts at the surface ocean (or land) at the expense of the enthalpy from the ocean, diabatic cooling occurs at the surface. Figure 5 shows diabatic cooling due to melting snow at the surface. The global annual mean net effect is −0.7 W m−2. Therefore, depending on the frequency of snow occurrence, when it is averaged over the globe and a year, snow warms the atmosphere (Fig. 4 right) and cools the surface (Fig. 5) up to ~1 W m−2 compared to diabatic heating by rain.
5. Discussion and summary
Energy and water budget equations of the atmosphere that explicitly consider the velocity of water vapor, liquid, and ice particles, and rain and snow and their diabatic heating are formulated. Differences of the equations [Eq. (38)] derived in this study from equations used in earlier studies are 1) diabatic heating rates due to rain and snow are expressed by separate terms, 2) diabatic heating rate due to net condensation of nonprecipitating hydrometeors is included, and 3) hydrometeors can be advected with a velocity different from the velocity of dry air.
Equation (38) indicates that changing the spatial scale from global to regional to understand regional energy balance in the atmosphere increases the complexity of tracking energy flow significantly. In addition, accounting for all energy flux components in balancing regional energy requires observations that are not easily available. Equation (38) requires the knowledge of where hydrometeors are formed and how much they are transported horizontally.
Even though energy fluxes by hydrometeors in a regional energy budget are difficult to estimate, there are some observations that can be used for such an estimate. Spaceborne radar can provide vertical profiles of hydrometeors and height at which precipitating hydrometeors form (e.g., Tao et al. 2006). In addition, understanding source and sink regions of hydrometeors helps in the understanding of regional energy balance residuals when energy data products are integrated. Therefore, achieving regional water mass balance with satellite date products is necessary (e.g., Brown and Kummerow 2014). Cloud objects (Xu et al. 2005; Duncan et al. 2014; White et al. 2017) derived from high temporal observations by new geostationary satellites are also expected to help in the understanding of where clouds are generated and dissipated.
We investigated water mass balance and the effect of hydrometeor horizontal transport and diabatic heating on the regional energy budget in the atmosphere. To understand the effect of hydrometeor transport, we considered three ideal cases. When all water vapor is transported out of the column, when it forms ice crystals that are transported out, and when ice crystals subsequently fall out. At times, the energy in the atmosphere column can be more than 100 W m−2 larger from the energy in the column without ice crystal transport. The ice water content of an anvil formed in the tropics can be close to 0.1 g m−3 at a 10-km altitude (e.g., McFarquhar and Heymsfield 1996). The water vapor concentration of a tropical standard atmosphere is about 0.1 g m−3 at 350 hPa and can be 0.3 g m−3 when water vapor is saturated relative to ice. While the extreme condition considered here is not realistic, an additional water mass convergence, which also arises to balance regional water mass, associated with the horizontal transport of hydrometeors and diabatic heating can reduce regional energy budget residuals shown in Kato et al. (2016). Importantly, the sign of the energy budget change, to increase energy in the atmospheric column where hydrometeors are formed and exported and to reduce energy where hydrometeors are imported and evaporated, implies the correct direction to reduce the energy budget residual in the tropics.
The effect of enthalpy transport associated with water mass transfer through the atmosphere ocean boundary is to cool the atmosphere and to warm the ocean when the enthalpy is averaged over the global ocean. There is no effect of the enthalpy transport to the atmosphere and ocean combined system and the effect is independent of the choice of reference temperature, as long as the reference temperatures for the atmosphere and ocean are the same.
The enthalpy of vaporization used for the conversion of precipitation rate to diabatic heating rate depends on the temperature at which water vapor condenses. However, the temperature dependence of diabatic heating is offset by the enthalpy transferred from the atmosphere to hydrometeors and to water vapor. For a closed system in which precipitation and evaporation balances, the net effect of temperature dependence of the enthalpy of vaporization vanishes. As a result, the diabatic heating rate by precipitation computed by a constant enthalpy of vaporization at 0°C differs less than 1 W m−2 in the global annual mean value. The temperature threshold of melting snow to form rain does not affect diabatic heating by precipitation very much, but the phase of precipitation alters the regional atmospheric energy budget by up to ~15 W m−2.
In addition to causes that might lead to an energy budget residual investigated in this study, inconsistent temperature and humidity profiles used in different products can also introduce an energy budget residual. For example, differences in temperature and humidity profiles used for surface irradiance, sensible and latent heat fluxes, and moist static energy horizontal transport computations can introduce energy balance residual. While the inconsistency can be avoided by using energy fluxes from a reanalysis product, the difference between observed TOA irradiances and TOA irradiances from some reanalysis products is at times quite large (e.g., Trenberth and Fasullo 2013; Hinkelman 2019). Although the magnitude of residual caused by inconsistent temperature and humidity profiles is unknown, closing global and regional energy budget will require a coordinated effort among different energy flux data providers.
Acknowledgments
We thank Drs. Peter Bannon of the Pennsylvania State University, Rupert Klein of Freie Universität, and Olivier Pauluis of New York University for providing comments on earlier versions, three anonymous reviewers for providing useful comments on the manuscript, and Dr. Rémy Roca of Laboratoire d’Etudes en Géophysique et Océanographie Spatiales for encouragement to work on this topic. The WOA 2013 V2 ocean climatology data product was downloaded from https://www.nodc.noaa.gov/cgi-bin/OC5/woa13/woa13.pl?parameter=t, GPCP data were downloaded from http://gpcp.umd.edu/, and SeaFlux data are downloaded from https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00973. The efforts of SK, NL, FR, and DR for this research were supported by the NASA CERES project. The efforts of JF in this work were supported by NASA Award 80NSSC17K0565, and by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy’s Office of Biological & Environmental Research (BER) via National Science Foundation IA 1844590. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
APPENDIX A
List of Symbols
Column-integrated net conversion rates from vapor to liquid, vapor to ice, and liquid to ice | |
cp | Specific heat capacity of moist air at constant pressure including dry air, water vapor, liquid particle, ice particle, rain, and snow |
Specific heat capacity of dry air, water vapor, and moist air at constant pressure | |
cυ | Specific heat capacity of water vapor at constant pressure |
cl, ci, cr, cs, cχ | Specific heat capacity of liquid particle, ice crystals, rain, snow, and χ; cr = cl and cs = ci |
cw | Specific heat capacity of liquid water or ice |
Evaporation rate of water vapor at surface | |
FSH, FLH | Sensible and latent heat flux at the surface defined positive downward |
Ffallout | Enthalpy flux at the surface associated with water mass transport by precipitation |
Fυ | Enthalpy flux associated with evaporation of water vapor from surface to the atmosphere |
FΦ | Potential energy flux caused by horizontal and vertical divergence of water mass by water vapor and hydrometeor velocities deviating from dry-air velocity |
g | Acceleration due to gravity |
h | Enthalpy |
Jdif, Jdif,k, Jdif,T | Rate of energy change per unit mass due to diffusion, diffusion of kinetic energy, and diffusion of enthalpy |
Jfallout, Jfallout,k, Jfallout,T | Rate of energy change per unit mass due to fallout, fallout of kinetic energy, and fallout of enthalpy |
Jfr | Diabatic heating rate per unit mass due to friction |
Jm | Diabatic heating rate per unit mass due to radiation and conduction |
Jphase | Diabatic heating rate per unit mass due to water phase change |
Jr | Mass flux of water vapor and hydrometeors due to their velocities relative to dry-air velocity and mixing ratio gradients |
Diabatic heating rate per unit mass of dry air containing water vapor (and other absorbing gases) and hydrometeors due to radiation, absorption and emission by moist air, liquid and ice particles, rain, and snow | |
Jtrans | Diabatic heating rate per unit mass due to conduction |
k | Kinetic energy including all constituents |
ka, kυ, kl, ki, kr, ks | Kinetic energy of dry air, water vapor, liquid particle, ice particle, rain, and snow |
kT | Thermal conductivity |
lυ, ls, lf | Enthalpy of vaporization, sublimation, and fusion of water |
lυ0 | Enthalpy of vaporization at 0°C |
Ma, Ma,sfc | Dry-air mass per unit area and dry-air mass per unit area at the surface |
ma, mυ, ml, mi, mr, ms, m | Mass of dry air, water vapor, liquid particle, ice particle, rain, snow, and χ |
Precipitation rate including rain and snow at surface | |
Precipitation rate of rain and snow at surface | |
Column-integrated net conversion rates from vapor to rain, vapor to snow, rain to ice, rain to snow, and liquid particles to snow | |
p, psfc, p(0) | Pressure, surface pressure, and pressure at the top boundary |
pa, pυ | Dry-air pressure and water vapor pressure |
Rtoa, Rsfc | Net irradiance at the top-of-atmosphere and surface |
r | Sum of the mixing ratio of water vapor, liquid and ice crystals, rain drops and snowflakes |
rυ, rl, ri, rr, rs | Mixing ratio of water vapor, liquid particle, ice crystals, rain drops, and snowflakes |
Production rate of water vapor, liquid particle, ice particle, rain drops, and snowflakes per unit mass | |
Net condensation rate of water vapor to form liquid particle and rain | |
Net deposition rate of water vapor to form ice particle and snow | |
Net fusion rate of liquid particle to form ice particles, rain to form ice particles, rain to form snow, and liquid particle to form snow | |
T | Temperature of dry air, water vapor, liquid particles, ice crystals, rain drops, and snow flakes |
T750, T0 | 750-hPa temperature and reference temperature |
Tskin | Surface skin temperature |
Tp | Temperature of precipitating hydrometeors |
Tw | Wet-bulb temperature |
Mass-weighted mean temperature of the atmosphere | |
Mass-weighted mean temperature of the ocean | |
t | Time |
Ua | Horizontal velocity of dry air |
Uχ | Horizontal velocity of χ |
Uυ, Ul, Ui, Ur, Us | Horizontal velocity of water vapor, liquid particle, ice particle, rain, and snow = Ua + vχ |
vχ | Horizontal velocity of constituent χ relative to the dry-air horizontal velocity |
vυ, vl, vi, vr, vs | Horizontal velocity of water vapor, liquid particle, ice particle, rain, and snow relative to the dry-air horizontal velocity |
wχ | Vertical velocity of constituent χ relative to the dry-air vertical velocity |
wυ, wl, wi, wr, ws | Vertical velocity of water vapor, liquid particle, ice particle, rain, and snow relative to the dry-air vertical velocity |
z, z(0) | Height and height of the top boundary |
αm | Specific volume of moist air |
χ | Generic flow property per unit dry-air mass |
Rate of production of χ | |
Φ, Φs | Geopotential and surface geopotential |
η | Vertical coordinate, dry-air mass per unit area |
ρ | Density of air including water vapor and hydrometeors |
ρa, ρυ, ρl, ρi, ρr, ρ | Density of dry air, water vapor, liquid particles, ice crystals, rain drops, and snow flakes |
ωa, ωχ | Vertical velocity of dry air and χ |
APPENDIX B
Change of χ in the Dry-Air Parcel
APPENDIX C
Thermodynamic Equation
APPENDIX D
Water Mass and Moist Static Energy Equations
APPENDIX E
Enthalpy Flux Associated with Water Mass Transfer through the Surface Boundary
APPENDIX F
Conversion of Precipitation Rate to Diabatic Heating Rate
We assume that precipitation is formed at the temperature of 750-hPa height T750. and that the temperature of precipitating hydrometeors is the 2-m wet-bulb temperature when they fall on the surface. The temperature of the precipitating hydrometeors changes from T750 to Tw and the enthalpy is provided by the atmosphere. The diabatic heating rate depends on the phase of the hydrometeors.
- Precip. 1: T750 ≥ −10°C and Tw ≥ 0°C (rain at 750 hPa to the surface)
- Precip. 2: T750 ≥ −10°C and Tw < 0°C (rain at 750 hPa and snow at the surface. Hydrometeors cool from T750 to Tw, freeze at 0°C, and cool to Tw)
- Precip. 3: T750 < −10°C and Tw ≥ 0°C (snow at 750 hPa and rain at the surface. Hydrometeors warm from T750 to 0°C, melt, and warm to Tw)
- Precip. 4: T750 < −10°C and Tw < 0°C (snow at 750 hPa to the surface)
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