commonchaffinch @commonchaffinch@qoto.org

#carbon_sequestration
#review

In 2022, Kimberly A. Novick from the O'Neill School of Public and Environmental Affairs in Indiana University—Bloomington and coauthors discussed opportunities for ecosystem observations to inform nature based carbon sequestration solutions (NbCS).

The study "outline[s] steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies."

The study envisions the creation of "gold-standard datasets" that represent a full suite of carbon stock and flux measurements, including NbCS "treatments", baseline controls, and information about historical land use. The sources of the datasets include flux tower, survey, remote sensing, and models.

Opportunities:

(1) Flux tower data with limited spatial fingerprint may be combined with broader tree survey data.

(2) We do not have spatially explicit maps of cropland NbCS mitigation potentials, and do not know where climate conditions may favor or disfavor the use of cover crops to enhance carbon uptake.

(3) Coastal sequestration in tidal wetlands and seagrass are promising opportunities (~25 Tg CO2e year-1). Flux towers can be used to analyze the impacts of optimizing NbCS in carbon uptake and sequestration.

(4) Next-generation remote sensing measurements include solar-induced fluorescence, column-averaged atmospheric CO2, instruments for sensing ecosystem water stress (ECOSTRESS), microwave data on canopy water content. Some of these are at scales that match individual farms. There are also drone-mounted instruments. These can be merged with flux tower data following past approach like machine learning, but for a specific region to produce more accurate regional baseline maps.

(5) Reduce uncertainty in ecosystem models. Price in the uncertainty into market systems. How does high resolution simulations improve results. Model-data assimulation for near term ecological forecasting and landscape scale model-data fusion.

(6) NbCS projects modify local water and energy cycles, which makes it necessary to consider potential negative consequences in these that consistitute trade-offs with the global climate benefit.

(7) Design and validation of new market structures and inclusivity of solutions.

Case study

(1) The benefit-cost trade off of establishing a flux tower site for monitoring NbCS project is calculated.

(2) A conceptual diagram for combining tree intentory, soil cares, static chambers, flux tower, and remote sensing to create project carbon grids for monitoring purpose.

https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16156

#programming_memo

libicui18n.so.58 is an ICU library image - International Components for Unicode.

#杂感
似乎现在才渐渐开始理解使用已被证明的方法、可重复性、数据质量的重要性。文献最重要的部分往往是方法，概念不是真的，同一个概念可以对应不同的数学变量，而真正的现象体现在数学变量上。

#programming_memo

To cope with memory limit when working on large netcdf datasets, `xarray.open_dataset(..., chunks = {'time': ...})` works, but rechunking the data after the read statement does not work.

#programming_memo

Tests for my code must have 100% coverage, that is, make sure every line of the code is run at least once. This can be achieved by using coverage.py with tox.

The article also talks about preparing and uploading the package to PyPi using Poetry, pretty formatting using black, creating release tags on Github, CI/CD using Github Actions, and adding badges to README.md.

https://mathspp.com/blog/how-to-create-a-python-package-in-2022#a-simple-tox-configuration

#misc

I think people in science like people who explore and measure the external world.

#research_diary
#urban_heat_islands

I was plotting a generic map of daytime urban heat island intensity (UHII) today (left, color goes from slightly below 0 to 2.8 degrees). The map turned out to be vastly different from the previously reported spatial patterns, where the UHII clearly increases from the west to the east (right, from Fig. 1 of Zhao et al. 2014, DOI: 10.1038/nature13462).

Then I realized my variable was 2m air temperature, and their variable was land surface temperature. So I recalled another paper that showed the west-east divide in UHII for 2m air temperature (Fig. 3 of Zhao et al. 2018, DOI: 10.1088/1748-9326/aa9f73). As expected, there was no east-west divide in the UHII shown in the barplot.

Are the well-known evaporative and/or convective cooling effects on UHII in the west only applicable to the land surface temperature?

#urban_heat_islands
#heat_waves

In 2018, Lei Zhao from Princeton University and coauthors analyzed the effect of heat waves on the magnitudes of urban heat island effect.

All the findings were based on Community Earth System Models over 65 cities in North America. The heat waves were defined using the rural land unit’s temperature. The analysis were conducted based on both 2m and land surface temperatures, and the results were consistent. However, note the convective term’s contribution to land surface temperature is to the opposite of the 2m temperature.

The major conclusions are as follows:

1. During heat waves, the urban heat island magnitude is either not changed, or exacerbated.

2. The exacerbation happens to daytime temperature in the humid eastern U.S. in the present-day climate, and is caused by the evaporative effect and the increased anthropogenic waste heat from air conditioning. The evaporative effect is as such: the ample water availability in this region allows increased evaporative cooling in the rural regions during a heat wave, but the urban region, with reduced vegetation, does not have this benefit.

In the future climate, precipitation increases in this region in CESM, such that the urban land unit becomes amply watered and no longer suffers from the lack of evaporative cooling.

3. The lack of change happens to daytime temperature in the arid southeastern U.S. in the present-day climate, where the evaporative cooling contrast does not exist.

In the future climate, precipitation increases in this region in CESM create evaporative cooling contrast, and makes the urban heat island effect stronger during heat waves.

4. The exacerbation of nighttime temperature happens to all regions in the present and future climate, and is mainly caused by the increased use of air conditioning.

https://iopscience.iop.org/article/10.1088/1748-9326/aa9f73/meta

#urban_heat_islands
#physical_mechanisms

The 2014 study by Xuhui Lee from Nanjing University of Information Science and Technology, China, and coauthors investigated the drivers of urban heat island effect using 65 cities across North America.

Usually, urban heat island effect - expressed as the temperature difference between urban and rural areas, $\Delta$T - is considered to be primarily created by the reduced evaporative cooling over the urban surface.

However, Lee and coauthors showed that variations in $\Delta$T, across space, are rather created by variations in convective efficiency, which is determined by aerodynamic resistance, or surface roughness. The cities in humid regions are aerodynamically smoother than their surrounding rural areas, resulting in less efficient transfer of heat to the lower atmosphere. The cities in dry regions, on the country, are aerodynamically rougher than their surrounding rural areas, resulting in more efficient transfer of heat to the lower atmosphere. Therefore, the urban heat island effect is more important for the humid cities, and albedo management is an important means to mitigate urban heat island effect.

Background

The contributors to urban heat island effect includes (1) reduced evaporative cooling, (2) waste heat from human activities, (3) (sometimes) changed albedo, (4) daytime heat storage and nighttime heat release by buildlings, which is usually greater than by vegetation and soil, (5) energy redistribution through convection between the land surface and the atmospheric boundary layer. Among these, (1) and (5) are natural factors, and (2)(3)(4) are morphological and anthropogenic aspects of the city itself.

Method

The urban heat island effect was quantified in two ways. First, via the NASA MODIS satellite land surface temperature (LST) from 2003 to 2012 - note this is different from air temperature. Second, via a climate model (the Community Earth System Model) run at 0.23 x 0.31 degrees resolution to reflect individual cities from 1972 to 2004.

For the MODIS data, nine urban pixels (3x3) were selected from the city center, and were paired with 1-3 patches of 3x3 to 7x7 pixels in the surrounding rural land. Water pixels were excluded. [Isn't this too few?]

The climate model does not have a urban core definition, because the urban core and the rural areas are different land units, not spatially distinguished, in the same grid cell. [Now this clearly suggests potential biases in the attribution results due to model deficiencies. The study also used LST, and the findings would probably be less evident if air temperature were used]

Partition of the contributions from terms (1)-(5) were achieved via the method described in ref. 19. The partitioning equation is shown in the paper, and appears to depend on land surface model parameters. Aerodynamic resistance is calculated as a function of the thermal gradient between the temperature at the surface and at reference height, and the sensible heat flux.

Results

1. Simple regression across space between factors and the MODIS LST shows that annual mean nighttime $\Delta$T is positively correlated with the logarithm of population, but not significantly correlated with the climate (precipitation, solar radiation, temperature). On the other hand, the annual mean dayttime $\Delta$T is strongly positively correlated with precipitation, and weakly correlated with population.

Geographically, the visual effect is that the nighttime $\Delta$T does not differ much between the east and west, but the daytime $\Delta$T is clearly bigger for the eastern cities (Fig. 1).

These findings are also well-supported by past studies (refs 14,15,16,17)

[This result seems to suggest that nighttime $\Delta$T is mainly controlled by anthropogenic heat release, whereas daytime mainly by the natural evaporative and convective factors.]

2. The attribution plot, Fig. 2, shows that

(1) nighttime $\Delta$T is dominated by the evaporative term and the building storage term.

(2) for dayttime $\Delta$T, the evaporative term always contributed positively. The convective term contributed most positively in the southeastern U.S., moderately positively in the northern temperate zones, and negatively in the southeastern U.S. Interestingly, the radiation term (albedo) is negative in the southeastern U.S., suggesting that the cities increased the albedo of the surface and reflected more sunlight, but positive elsewhere.

3. Fig. 3 further shows the spatial correlations between the individual terms and precipitation. It is clear that the positive correlation between daytime $\Delta$T and precipitation is contributed mainly by the convection term. In fact, the covariance analysis shows that the convection term explains 200% of the covariance, whereas the other terms together explains -100%.

In rural lands in the humid areas, the aerodynamic roughness is greater, meaning lower aerodynamic resistance (39 s m-1) than the urban counterpart (62 s m-1). Lower aerodynamic resistance means greater convective efficiency.

In past studies, the urban cool island effect in dry regions was explained by the evaporative cooling of urban greenspace (refs 15, 16, 17). However, in the current results, a lot of the cooled pixels are vegetation free.

4. Fig. 4 differs from Figs. 1-3 by showing the temporal sensitivity of dayttime and nighttime $\Delta$T to precipitation. However, there is conflict between the MODIS data and the climate model with regard to whether the dayttime $\Delta$T in the majority of the cities is positively or negatively correlated with precipitation. The conflict may be due to the short length of the MODIS data, or the bias of the climate model. Nonetheless, the precipitation-induced cooling effect becomes more evident as annual mean precipitation increases, i.e. towards the east. This is clearly induced by evaporative cooling.

https://www.nature.com/articles/nature13462

#programming_memo

写条件语句的时候，要修改的变量一定不能出现在条件语句里。

#杂感

参加了一个大项目的panel review，90%的内容都听不懂，怀疑自己一辈子也不可能达到管理那个级别项目的水平。且不提跨领域的海洋生物地球化学循环和气候系统的tipping point，只是和陆面相关的机器学习、野火、植物光合机理和对水文&碳循环的控制、模型权重这些，都是只知道关键词而无法跟上逻辑。我能学会这些吗，在学界的pay/time balance情况下，我有必要学会这些吗？

#ecosystem_resilience
#hydroclimate_extremes
#ecological_extremes

In 2014, J. Zscheischler and co-authors conducted a continental-scale analysis on extreme events in gross primary productivity (GPP). The datasets used included a machine learning based construction, a semi-empirical, and two land surface models (OCN and LPJmL).

They found a few important phenomena:

(1) The 50 largest positive and negative GPP extremes accounted for most of the variations in continental GPP variation.

* That is, the extreme events, though limited in number, are very important for interannual variability in GPP.

(2) The spatial extents of the GPP extremes played a larger role on the impact of the event, than the duration or maximal GPP.

(3) Water scarcity was the most important cause of negative GPP extremes. Heat waves played a secondary role. In Europe, South America, and Oceania, fire was a third important factor.

* That is, GPP extremes happened most often when there is drought, followed by heat waves, and finally, in some continents, fires.

* It's interesting that the heat wave seemed to account for the GPP extremes best in Russia. Is this because the vegetation there are adapted to cold conditions?

https://bg.copernicus.org/articles/11/2909/2014/

#urban_vegetation
#urban_rural_differences
#vegetation_cover

(The vegetation cover was quantified as Enhanced Vegetation Index (EVI)).

In 2018, Shuqing Zhao analyzed the direct and indirect effects of urbanization on vegetation growth.  The direct effect of urbanization is through land cover change. The indirect effect is through the urban environment. On average, the indirect vegetation growth enhancement in the U.S. offset 29.2%, 29.5%, and 31.0% of the growth reductions caused by impervious area replacement.

Background

There has been a debate on whether the urban environment enhances or suppresses vegetation growth. High air temperature, low soil water content are stress factors on vegetation. But fertilization, irrigation, introduction of non-native species, urban heat island, climate change, and atmospheric chemistry change like ozone and carbon dioxide were found to enhance vegetation growth by later studies. The authors previous study [Shuqing Zhao et al. PNAS 133 (22) 6313-6138 https://doi.org/10.1073/pnas.1602312113] already demonstrated that vegetation growth enhancement existed in 32 major cities in China. In this study, they would like to verify the same effect existed in USA cities.

Some useful information

The Northeast (NE) and Southeast (SE) U.S. are mainly covered by forests. The East North Central (ENC) and Central (C) are covered by a large cropland area. The West North Central (WNC) are the South are heavily covered by crops, pasture, and grassland. The Southwest (SW) is dominated by shrubs. The West (W) and Northwest (NW) are mostly covered by forests and shrubs.

Data

377 metropolitan statistical areas in CONUS

EVI 250 m product (MOD13Q1) from MODIS, averaged over the growing season (frost-free days) in the years 2001, 2006, and 2011

Impervious area was originally 30 m and interpolated to 250 m

The pixels that were water body, or had elevations > 50 m above the highest elevation of urban core (impervious percentage > 50%) were excluded to remove the effect of water body and elevation

Methods

(1) Definitions of the relative direct effect, the relative indirect effect, and the growth offset

The conceptual decomposition of the indirect and direct effects of impervious area is:

V_{obs} = (1 + \omega)(1 - \beta)V_v + \beta V_{nv}

, where V_{obs} is the observed vegetation index, V_{v} is the background vegetation index without urbanization, V_{nv} is the vegetation index of the pixel when it is completely impervious, \beta is the percent impervious area, and \omega is the effect of urbanization on vegetation growth. Note that the pixels with no impervious area may still have enhanced vegetation growth in an urban area, but the number of these pixels is relatively small.

V_{nv} was obtained as the mean EVI of the fully urbanized pixels over all the MSAs, and found equal to 0.0064. V_v was obtained by polynomial regression between V_{obs} and \beta (V_{obs} = V_v + a_1 * \beta + a2_ * \beta^2 + a3 * \beta^3).

The “background” vegetation growth, without vegetation impacts, can then be interpolated from V_v and V_{nv} as:

V_{zi} = (1 - \beta)V_v + \beta V_{nv}

, with V_{nv} being equal to 0.0064 in this study. So the relative direct urbanization effect on vegetation growth is:

\omega_d = (V_{zi} - V_v) / V_v * 100%

, and the relative indirect urbanization effect on vegetation growth is

\omega_i = (V_{obs} - V_{zi}) / V_{zi} * 100%

Finally, “growth offset” is defined as the ratio of the absolute indirect effect to the absolute direct effect, in order to quantify how the indirect effect compensates for or worsen the direct effect:

\tau = (V_{obs} - V_{zi}) / (V_v - V_{zi}) * 100%

(2) Definition of urban intensity

The urban intensity of a pixel is defined as the percentage of developed imperviousness surfaces in the pixel.

It seems they derived V_v separately for each city and each year, based on Fig. 2b and Section 3.2. However, it is not clear if they controlled for the land cover effect by separately deriving for each land cover type.

Results

Fig.2c shows quite clearly that in the vast majority of pixels, the V_{obs} is higher than the expected value from impervious area (V_{zi}) - i.e. “urban pixels are often greener than expected given the amount of paved surface they contain”.

The estimated \omega_i values appear to increase slightly with \beta, whereas the \tau values decrease slightly. That is, as the impervious areas become higher and the vegetation becomes sparser, the compensated growth becomes larger relative to the actual growth, but becomes smaller relative to the amount of missed growth due to the direct effect.

In terms regional differences, unsurprisingly, the unperturbed vegetation (V_v) is the highest for the forested NE and the cropped ENC and C, and the lowest for the arid SW and W.

In all the individual regions, the growth offset (\tau) decreases with higher impervious area (\beta). The SW has the highest \tau, whereas the C, NE, and SE have the lowest. This may be because the western U.S. is more heavily irrigated?

In the eastern regions (ENC, S, SE, NE, C), the relative indirect effect (\omega_i) increases with impervious area (\beta), but in the western regions (NW, WNC, W, SW), the relative indirect effect is the highest at medium impervious levels.

Discussion

Their results demonstrated the indirect effect, which was rarely done by past regional scale studies. Also, their results were consistent with most ground observations that showed urbanization to enhance vegetation growth. A few studies (e.g. in temperate zone cities in Europe) showed negative impacts of urbanization on vegetation growth.

The urban to rural gradient include factors like terrain, soils, species, air pollutants, temperature, CO2 enrichment, N deposition, ozone, and traffic volume. Therefore, the potential causes of influences are myriad and complicated.

The unique hump-shaped curve in the west may be because there are too little effort spent on vegetation planting on city fringes, and the amount of impervious area is too high to allow vegetation in the urban center. As a result, the medium dense areas, intentional planting and urban management effectively increase the vegetation index. On the other hand, man-made changes in species and management in humid cities would not cause any obvious increase in vegetation index, since the background is already quite high.

https://onlinelibrary.wiley.com/doi/10.1111/gcb.14317

#urban_vegetation
#urban_rural_differences
#phenology

These two studies in 2021 and 2019 show that, in urban areas, the start-of-season usually occur earlier, and end-of-season latter, overall enhancing the length of the growing season.

There are, however, exceptions to this rule. The responses of start and end of season depend on the background climate of the urban area (latitude, spring day- and nighttime temperatures). The northern cities have greater urban-rural distinction than the southern cities.

The corresponding authors to these studies were Shuqing Zhao from Peking University, and Xinchang Zhang from Guangzhou University & Qinchuang Xin from Chinese Academy of Sciences and Sun-Yat Sen University.

https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.15602?

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JG005262