The main source of ocean heat is sunlight. Additionally, clouds, water vapor, and greenhouse gases emit heat that they have absorbed, and some of that heat energy enters the ocean. Waves, tides, and currents constantly mix the ocean, moving heat from warmer to cooler latitudes and to deeper levels. The heat energy eventually re-enters the rest of the Earth system by melting ice shelves, evaporating water, or directly reheating the atmosphere.
Thus, heat energy in the ocean can warm the planet for decades after it was absorbed. If the ocean absorbs more heat than it releases, its heat content increases. Knowing how much heat energy the ocean absorbs and releases is essential for understanding and modeling global climate.
As water warms, it expands, so estimates for ocean temperature can be deduced from sea surface heights. To get a more complete picture of ocean heat content at different depths, scientists and engineers also use a range of in situ temperature-sensing instruments.
Known as Argo floats, the sensors drift through the ocean at different depths. Every 10 days or so, according to their programmed instructions, they rise through the water, recording temperature and salinity as they ascend. When a float reaches the surface, it sends its location and other information to scientists via satellite, and then descends again. Seals have even been fitted with instruments to obtain temperatures in areas that are difficult to reach.
Weise, California Sea Grant. Scientists constantly compare data from satellites, floats, and probes to verify that the values they produce make sense. They process the range of measurements to calculate an estimate for global average ocean heat content every three months. More than 90 percent of the warming that has happened on Earth over the past 50 years has occurred in the ocean. Recent studies estimate that warming of the upper oceans accounts for about 63 percent of the total increase in the amount of stored heat in the climate system from to , and warming from meters down to the ocean floor adds about another 30 percent.
Annual ocean heat content compared to the average from , based on multiple data sets: surface to depths of meters 2, feet in shades of red, orange, and yellow; from , meters 6, feet in shades of green and blue; and below 6, feet 2, meters as a gray wedge. See original figure for details about data sources and uncertainty.
Less than a watt per square meter might seem like a small change, but multiplied by the surface area of the ocean more than million square kilometers , that translates into an enormous global energy imbalance. It means that while the atmosphere has been spared from the full extent of global warming for now, heat already stored in the ocean will eventually be released, committing Earth to additional warming in the future.
In the present, warming of ocean water is raising global sea level because water expands when it warms. Combined with water from melting glaciers on land, the rising sea threatens natural ecosystems and human structures near coastlines around the world.
Finally, warming ocean waters threaten marine ecosystems and human livelihoods. Air over the tropical oceans is drier than you might think. Although both the air and water may be warm and calm, evaporation can take place because the air is not at percent relative humidity. Silently and invisibly, water changes from liquid to vapor and enters the atmosphere.
The energy required to make this change comes from the sun, and this energy is lying in wait — latent — ready to be released when the vapor is condensed into liquid again. This happens in rising air in a cloud or thunderstorm. Nature Education Knowledge 2 , Takayabu, Y. Large-scale cloud disturbances associated with equatorial waves. Part I: Spectral features of the cloud disturbances. Journal of the Meteorological Society of Japan 72 , Wallace, J.
Observational evidence of Kelvin waves in the tropical stratosphere. Journal of the Atmospheric Sciences 25 , Wheeler, M. Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber-frequency domain. Journal of the Atmospheric Sciences 56 , Real-time monitoring and prediction of modes of coherent synoptic to intraseasonal tropical variability. Yanai, M. Stratospheric wave disturbances propagating over the equatorial Pacific.
Zangvil, A. Temporal and spatial behavior of large-scale disturbances in tropical cloudiness deduced from satellite brightness data. Global Change: An Overview. Conservation of Biodiversity. Introduction to the Basic Drivers of Climate. Tropical Weather. Terrestrial Biomes. Causes and Consequences of Dispersal in Plants and Animals. Causes and Consequences of Biodiversity Declines. Disease Ecology. Coastal Dunes: Geomorphology. Coastal Processes and Beaches. Drip Water Hydrology and Speleothems.
Earth's Earliest Climate. El Nino's Grip on Climate. Large-Scale Ecology Introduction. Methane Hydrates and Contemporary Climate Change. Modeling Sea Level Rise. Ocean Acidification. Rivers and Streams - Water and Sediment in Motion.
Principles of Landscape Ecology. Spatial Ecology and Conservation. Restoration Ecology. Energy Economics in Ecosystems. Earth's Ferrous Wheel. The Ecology of Fire. Tropical Weather By: Adam H. Citation: Sobel, A. Nature Education Knowledge 3 12 What makes tropical weather different from that at higher latitudes? Which is more predictable? Aa Aa Aa. References and Recommended Reading Boer, G. Riehl, H. Citation: Journal of the Atmospheric Sciences 68, 8; Given the consecutive sampling and spatial coverage, the Guillermo dataset provides the opportunity for studying fundamental problems associated with the impacts of deep convection and the role of the asymmetric mode in TC intensification.
However, relatively coarse resolution of the Doppler analyses in space and time still limits the interpretation of the basic physics. The storm-centered radar domain is a box extending km on a side with 2-km grid spacing and 20 km in the vertical with 1-km grid spacing.
The first level of useful data is at 1-km height because of ocean surface contamination. The TA radar reflectivity field used in this study has not been corrected for attenuation. We focus our attention mostly on the inner portion of the domain the eyewall, which is about 30 km from the radar on average to minimize these effects.
Note that, for many passes, two aircraft were used to construct the radar analysis, which will help reduce attenuation effects see Table 1 in Reasor et al.
The radar scanning strategies employed in the Guillermo sampling require a finite time separation between radial wind measurements in order to construct an accurate wind vector. Reasor et al. This radar dataset is used to perform a latent heat retrieval, described in detail in the next section. The technique for retrieving latent heat from airborne Doppler radar is based partly on the method of Roux and Roux and Ju These studies used simplified forms of the momentum and thermal energy equations, with individual terms or forcings estimated from radar observations, to deduce the pressure and temperature fields of squall lines.
We focus our attention on the computation of saturation [see appendix B of Roux and Ju ] and the use of the thermal energy equation. Several advancements in the basic algorithm are developed and presented below, including a analyzing the scheme within the dynamically consistent framework of a numerical model, b identifying sensitivities through the use of ancillary data sources, and c developing a water budget storage term parameterization.
To prove the efficacy of the retrieval method, output from a nonhydrostatic, full-physics, quasi-cloud-resolving model simulation of Hurricane Bonnie at 2-km horizontal grid spacing Braun et al. The focus here will be on a 1-h period of the simulation domain size of approximately km 2 in the horizontal extending to At this time, the simulated storm was intensifying despite the influence of northwesterly vertical wind shear that resulted in an asymmetric distribution of convection [see Braun et al.
Although the simulated TC does not replicate the observed storm, the dynamically consistent nature of the model budgets allows the assessment of the qualitative and, to some degree, quantitative accuracy of the method. Gao et al. More real cases are needed to determine if the quantitative aspects of the Gao et al. As a result, we believe that testing the latent heat retrieval algorithm in the context of a numerical model provides a useful first step toward a reliable product.
The release of the latent heat of condensation occurs when water vapor changes phase to liquid water, which requires the air to be saturated. Therefore, for strong updrafts, analysis of the vertical momentum equation reveals that local buoyancy from the release of latent heat must be present to generate significant vertical wind speeds and accelerations Braun ; Eastin et al.
Therefore, an important question is the following: does a threshold of vertical velocity exist where saturation and the release of latent heat can be assumed? Figure 2 shows updraft cores defined as convective-scale vertical velocities that exceed 1. Levels above approximately 5. These data suggest that using a vertical velocity saturation threshold of approximately 5.
The numerical simulation of Hurricane Bonnie is used to calculate basic statistics on saturated vertical velocities on a grid point by grid point basis to support the observational data.
A similar result is found for downdrafts. This threshold should only be used as a guide as updrafts likely do not obey strict rules, but rather evolve through a continuum. Furthermore, the saturation threshold has uncertainty: the observational data shown in Fig. As a result, saturation cannot be assumed for the vast majority of updrafts and a large percentage of the total mass flux, which motivates the need for the determination of saturation through the algorithm described below.
Figure 3 shows a scatterplot of the relationship between Q net output from model and the source of cloud water saturation at model grid points where precipitation is produced between 0- and km heights in the first 9 min of the 1-h simulation period.
This subset of data is representative of the entire simulation and includes points. A height of 10 km is used as a cap for points in Fig. The points in Fig. There is a linear relationship between the two variables in Fig. The dominant mode of precipitation growth shown in Fig. Braun showed that in the azimuthal mean, the source of cloud water in the eyewall is immediately removed by precipitating hydrometeors, shown here on the grid point scale.
The off-linear scatter in Fig. Indeed, observations suggest that supercooled cloud liquid water can exist at altitudes of 12 km in deep convection located in the TC eyewall Black et al.
Figure 4 shows an example of the vertical structure of the relationship between Q net and the source of cloud water averaged over a representative eyewall convective cell in the Bonnie simulation. The source of cloud water matches very well with the net production of precipitation up to 5—6-km height melting zone. Above 6-km height, ice phase microphysics begins contributing to the formation of precipitation.
A similar vertical structure is found for other regions of the simulation domain. Knowledge of the possible microphysical sources of precipitation Rogers and Yau suggests that this is also true of TCs in nature.
That is, assuming that information on the water content and winds are available quasi-instantaneously, the saturation state of the air and the associated magnitude of the latent heat release described below can be determined at the same time.
Therefore, by using the signal to which the radar responds precipitating hydrometeors for 10 GHz , information on the saturation state at each grid point in the 3D Doppler domain can be retrieved. There are errors in associating the net production of precipitation with saturation in mixed phase regions of convection and for small values of Q net that could occur near cloud boundaries, for example. When applying the theory to radar observations, instrument errors are also possible because of resolution, nonhomogeneous beam filling, attenuation, and calibration.
Another source of error is the time separation between radar beam intersections discussed in section 2 , which violates the instantaneous assumption. However, the algorithm presented here is somewhat insensitive to these errors because information is only required on the condition of saturation, not the magnitude of that saturation.
Put another way, the algorithm is only dependent on knowing if precipitation is being produced, not on the precise value of precipitation production. Relying on Q net for quantitative purposes such as computing the latent heat magnitude can lead to significant errors because of the large uncertainty in single-frequency radar-derived water parameters see introduction; Gamache et al.
We focus on the qualitative nature of Q net to reduce the consequences of these errors, although dual-frequency radars show promise for quantitative retrievals of Q net in future studies. With the P-3 radar used in this study, substantially reduced errors in the latent heat magnitude can be achieved by using the radar estimates of vertical velocity directly described below rather than relying on Q net quantitatively.
Figure 5 presents a flowchart summarizing the main steps in the latent heat retrieval algorithm described above. Note that when applying the theory to radar observations, q p and V t shown in 2 are determined from the reflectivity using the empirical relationships described in section 4a. Previous studies employing a form of the retrieval method outlined above have been unable to calculate the storage term in 2 because of inadequate Doppler radar sampling and thus have assumed the system or the clouds were in a steady state Roux ; Roux and Ju ; Gamache et al.
In a storm-relative reference frame, both the cloud and system scales of motion are not steady state and significant error can be expected if assuming stationarity Gamache et al.
The Guillermo dataset is unique in that composite Doppler radar sampling was completed on average every 34 min, allowing estimation of the storage term.
However, it is found that using a min time increment for computing the storage term added no more information order of magnitude smaller than other terms to the precipitation budget than using the steady-state assumption.
This result is not surprising considering that the life cycle of a cloud is on the order of 30 min Houze We have demonstrated that Q net is a very good proxy for saturation in a numerical setting. In addition, a reduced form of the precipitation continuity equation with a parameterization of the storage term has been shown to provide a good diagnosis of the actual Q net output from the model.
An obvious question presents: what is the impact of these approximations on the derived latent heating? Figure 9 shows the impact of the storage term parameterization in terms of the azimuthal mean latent heating at the radius of maximum wind RMW for the Guillermo Doppler radar observations shown in the next section. This sensitivity analysis is shown here because our ultimate goal is to apply the algorithm to observations. Large changes to the azimuthal mean heating relative to the steady-state case are found when using the parameterization see Fig.
Recent research has shown that for simplified TC-like vortices, the azimuthal mean heating dominates the dynamics of TC intensification Nolan and Grasso ; Nolan et al.
In light of these results, the storage term sensitivity shown in Fig. Figure 10 shows the errors [according to 6 ] in computing latent heat by determining saturation using 2 and 5 and identifying where the values of Q net are greater than zero. The control is computing latent heat at grid points that are producing cloud water, which is required for air to be saturated.
Differences between the latent heating rates should be small and the expression in 4 is currently the only practical way to compute them from radar observations. The temporal mean error in Fig. The results described above demonstrate that the method for determining saturation in the latent heat retrieval is quite reasonable.
Validating this result using observations is difficult because of the lack of in situ data over the large swaths sampled by the radar. Using a combination of flight-level data and dropsondes offers the best avenue for validation and is left for future work.
Sensitivity tests and observational error analyses of the diagnostic heating expression in 4 are detailed in the next section. To compute Q net from Doppler radar, the total precipitation mixing ratio must be known, which is a summation of liquid water content LWC and ice water content IWC. To derive this quantity, in situ cloud particle data collected by NOAA P-3 aircraft near 4-km altitude in the intense stages of Hurricane Katrina are analyzed.
The cloud particle data are averaged over a period of 6 s in an attempt to match the sampling volumes of the particle probe and Doppler radar pulses R. Black , personal communication. Figure 11 shows a scatterplot of the relationship between reflectivity factor and LWC for the Katrina data.
Note that relationships between radar reflectivity factor and water content parameters are not unique and therefore uncertainty in Q net will exist. This uncertainty is similar to rainfall rate discussed in section 1 with random errors as large as a factor of 4 Doviak and Zrnic As mentioned in the previous section, however, the algorithm presented here is somewhat insensitive to these errors because information is only required on the condition of saturation sign of precipitation production term , not the magnitude of saturation precise value of precipitation production term.
Equation 2 is solved for Q net using the Guillermo Doppler analyses, the storage term parameterization in 5 , the computed precipitation mixing ratios described above, hydrometeor fall speed relations for a gamma distribution Ulbrich and Chilson ; Heymsfield et al.
Based on Fig. To compute the magnitude of latent heat released at saturated grid points in the radar domain, knowledge of the thermodynamic structure of convective cells is required, which is very difficult to obtain. To approximate the thermodynamic structure, a composite sounding derived from 10 high-altitude [using NASA aircraft that fly at altitudes of 10 and 20 km] dropsondes representative of eyewall convection in TCs is utilized.
The storms sampled were Hurricane Bonnie , Tropical Storm Chantal , Hurricane Gabrielle , Hurricane Erin , and Hurricane Humberto yielding 10 independent thermodynamic profiles of eyewall convection. The sampling of eyewall convection is verified using winds and relative humidity from the dropsondes as well as satellite infrared and passive microwave observations.
Discussion on the uncertainty associated with using a composite dropsonde is discussed below. To complete the latent heat calculation, the vertical velocities derived from the Doppler radar synthesis procedure are input to 4. The latent heat of condensation is capped at km altitude based on independent numerical simulation experiments and the structure of the cloud water source shown in Fig.
Figure 12 shows horizontal views of the derived latent heat field in Hurricane Guillermo averaged over all heights for each aircraft pass in Fig. The structure in Fig. Initially the latent heat field is quite asymmetric with convection displaced to the downshear quadrants of the storm due to the persistent vertical shear forcing of the vortex Reasor et al. This low-wavenumber latent heat asymmetry is coupled to the vorticity field Reasor et al. Several passes in Fig. Figure 12 also displays the vertical profile of the azimuthal mean latent heating at the RMW 30 km above the contour plot for each pass.
The altitude of peak heating varies between approximately 4 and 9 km including a distinct double maximum upper and lower levels present in several passes. This is the topic of Part II. There are two main calculations in the retrieval that require error analysis: the computation of the saturation state and the magnitude of the latent heat. The approximate errors associated with determining saturation are analyzed in section 3b , and thus the focus here is on the magnitude of the latent heat fields.
The magnitude of the latent heat is essentially a function of thermodynamic information temperature and pressure and vertical velocity. The uncertainty in the thermodynamic information is assessed by first gathering soundings from various regions eyewall and environment of the numerical simulation of Hurricane Bonnie and eyewall dropsonde observations in several storms see section 4a for the list of TCs.
These results indicate that the magnitude of the latent heat is not very sensitive to the details of the thermodynamic information in the eyewall of TCs. Sensitivity to the vertical velocity is much greater and is the most important parameter in the estimation of latent heat.
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